Context-aware decision making

ABSTRACT

A context-aware decision making system may include at least one processor circuit that is configured to obtain an environmental profile of an environment associated with a user. The at least one processor circuit is configured to determine a predicted behavior of the user based at least on the obtained environmental profile. The at least one processor circuit may be configured to determine the predicted behavior of the user using at least one predictive model associated with the user. The at least one processor circuit may be configured to perform an action related to the predicted behavior of the user, such as an action that facilitates the predicted behavior of the user, an action that impedes the predicted behavior of the user, and/or an action that provides information related to the predicted behavior of the user.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of U.S. Provisional PatentApplication Ser. No. 61/989,358, entitled “Context-Aware BehavioralFeedback,” filed on May 6, 2014, which is hereby incorporated byreference in its entirety for all purposes.

TECHNICAL FIELD

The present description relates generally to devices that providedecision making, and devices that provide context-aware decision makingand/or behavioral feedback.

BACKGROUND

According to some estimates, more than 30 billion devices will becapable of being connected by 2020. These devices may include sensordevices, wearable devices, computing devices, home appliances, and thelike. The devices may be configurable to interoperate with one or moreother devices, such as to collectively perform one or more tasks, e.g.on behalf of a user and/or an application.

BRIEF DESCRIPTION OF THE DRAWINGS

Certain features of the subject technology are set forth in the appendedclaims. However, for purpose of explanation, several embodiments of thesubject technology are set forth in the following figures.

FIG. 1 illustrates an example network environment in which acontext-aware decision making system may be implemented in accordancewith one or more implementations.

FIG. 2 illustrates a flow diagram of an example process of acontext-aware decision making system in accordance with one or moreimplementations.

FIG. 3 illustrates a flow diagram of an example process of acontext-aware decision making system in accordance with one or moreimplementations.

FIG. 4 illustrates a flow diagram of an example process of acontext-aware decision making system in accordance with one or moreimplementations.

FIG. 5 illustrates an example data flow of a context-aware decisionmaking system in accordance with one or more implementations.

FIG. 6 illustrates an example data flow of a context-aware decisionmaking system in accordance with one or more implementations.

FIG. 7 conceptually illustrates an example electronic system with whichone or more implementations of the subject technology can beimplemented.

FIG. 8A is a schematic block diagram illustrating one example ofcircuitry and software within a device (i.e., within a device housing)that supports at least one implementation of the subject technology.

FIG. 8B is a schematic block diagram illustrating another example ofcircuitry and software within a device (i.e., within a device housing)that supports at least one implementation of the subject technology.

FIG. 9 is a block diagram illustrating an example of bio-impactprocessing and dynamic underlying duties assignment that supports atleast one implementation of the subject technology.

FIG. 10 is a block diagram illustrating one example of triggering eventidentification based on all types of bio-impact profiles that supportsone or more implementations of the subject technology.

DETAILED DESCRIPTION

The detailed description set forth below is intended as a description ofvarious configurations of the subject technology and is not intended torepresent the only configurations in which the subject technology may bepracticed. The appended drawings are incorporated herein and constitutea part of the detailed description. The detailed description includesspecific details for the purpose of providing a thorough understandingof the subject technology. However, the subject technology is notlimited to the specific details set forth herein and may be practicedusing one or more implementations. In one or more instances, structuresand components are shown in block diagram form in order to avoidobscuring the concepts of the subject technology.

The subject context-aware decision making system collects and/or obtainsinformation regarding one or more users' behaviors, biometric data,and/or associated environmental information over a period of time. Theassociated environmental information may include, for example, adiscrete and/or descriptive location of the user, such as in a kitchenof the user's home, a time of day, information regarding one or moredevices that are proximal to the user, such as a refrigerator device,and the like. The users' behaviors may include online behaviors, such assubmitting a query to a search engine, and/or offline behaviors, such aseating breakfast. In one or more implementations, the subject systemuses the collected information to determine predicted and/or expectedbehaviors of a user, such as based at least on obtainable currentenvironmental information associated with the user and/or obtainablecurrent biometric data of the user. The subject system performscontext-aware decision making to determine an action that facilitatesthe predicted and/or expected behavior of the user, such as providingcontext-aware behavioral feedback and/or other information to the userthat relates to the predicted and/or expected behavior, and/orinitiating performance of an action that relates to the predicted and/orexpected behavior. In one or more implementations, the subject systemreceives a behavioral policy that is intended to guide behaviors of auser, such as a dietary policy, and the subject system performs actionsto conform predicted and/or expected behaviors of the user to behaviorsindicated by the behavioral policy.

The subject system uses the collected information to determine predictedand/or expected biometric data for a user, such as based at least onobtainable or detectable current environmental information associatedwith the user, and/or obtainable or detectable current behaviors of theuser. The system collects and/or monitors the current biometric data ofthe user and determines whether the current biometric data deviates fromthe predicted and/or expected biometric data. If the current biometricdata deviates from the predicted and/or expected biometric data, thesubject system provides context-aware decision making by performing anaction that facilitates conforming the current biometric data of theuser to the predicted and/or expected biometric data of the user. If thecurrent biometric data conforms to the predicted and/or expectedbiometric data, the subject system may perform an action to facilitatemaintaining the current biometric data for the user.

FIG. 1 illustrates an example network environment 100 in which acontext-aware decision making system may be implemented in accordancewith one or more implementations. Not all of the depicted components maybe used, however, and one or more implementations may include additionalcomponents not shown in the figure. Variations in the arrangement andtypes of the components may be made without departing from the spirit orscope of the claims as set forth herein. Additional, different or fewercomponents may be provided.

The network environment 100 includes an electronic device 102, a network108, one or more electronic sensor devices 106A-C, one or moreelectronic devices 112-C, an output device 114, and a service providerserver 110. The network 108 may include, and/or may be communicativelycoupled to, one or more of the Internet, a private network, a personalarea network, and/or any other networks. The network 108 may include oneor more wired or wireless network devices that facilitate communicationsof the electronic device 102, the electronic devices 112A-C, and/or theservice provider server 110, such as switch devices, router devices,relay devices, etc., and/or the network 108 may include one or moreserver devices. The electronic device 102 may establish a direct networkconnection, e.g. via Bluetooth, near-field communication (NFC), WiFiDirect, etc., with one or more of the electronic devices 112A-C and/orone or more of the electronic sensor devices 106A-C, withoutcommunicating through the network 108. The electronic sensor devices106A-C are each capable of communicating both directly and indirectlywith the electronic device 102. Indirectly both via the access points ofthe network 108 and via other of the electronic sensor devices 106A-C,e.g., via meshes or relaying. In one or more implementations, one ormore of the electronic sensor devices 106A-C may be directlycommunicatively coupled to the network 108.

The electronic device 102 is depicted in FIG. 1 as a smartphone.However, the electronic device 102 may be any network-connectable devicethat can receive sensor data, such as from one or more of the electronicsensor devices 106A-C. In one or more implementations, the electronicdevice 102 is, and/or includes, one or more of the electronic sensordevices 106A-C, such as an accelerometer, a heart rate measurementdevice, etc. The electronic device 102 may include a secure element forstoring user biometric information, biometric profiles, environmentalprofiles, predictive models, etc. The electronic device 102 mayaggregate data received from one or more of the electronic sensordevices 106A-C, and/or from one or more of the electronic devices112A-C, to generate one or more biometric profiles and/or one or moreenvironmental profiles for a user.

The electronic sensor device 106A is depicted in FIG. 1 as a smartwatch, the electronic sensor device 106B is depicted in FIG. 1 as smartglasses, and the electronic sensor device 106C is depicted in FIG. 1 asan arm band device. However, the electronic sensor devices 106A-C may beany devices that can obtain, or sense, information regarding a userand/or an environment associated with the user, such as biometricinformation, user activity information, user images, temperature,location, time of day, etc. For example, the electronic sensor devices106A-C may include network camera devices, home security devices, heartrate monitoring devices, implanted and/or implantable devices, wearabledevices, activity monitoring devices, blood flow monitoring devices,sleep monitoring devices, or generally any device that can provideinformation from which a user is identifiable.

In one or more implementations, one or more of the electronic sensordevices 106A-C may be one or more of: a shoe insert device that measuresweight changes of the user, such as while eating and visiting therestroom; a belt device that senses pressure changes from the user'sbody pressing on the belt device and that changes sizes to maintain aparticular pressure with respect to the user's body, such as when theuser gains and/or loses weight; or a scale device that provides theweight of the user. One or more of the electronic sensor devices 106A-Cmay include at least one processor circuit that can perform one or moreprocessing tasks described herein.

In one or more implementations, one or more of the electronic sensordevices 106A-C, such as the electronic sensor device 106B, includes asecure element for storing user biometric information, biometricprofiles, environmental profiles, predictive models, etc., e.g. in lieuof and/or in addition to, the electronic device 102. One or more of theelectronic sensor devices 106A-C, such as the electronic sensor device106B, may communicate directly with the network 108 and/or with one ormore of the electronic devices 112A-C, without communicating through theelectronic device 102.

The electronic devices 112A-C may be devices that are proximal or remoteto the electronic device 102, and/or to the electronic sensor devices106A-C, and that can provide information to, and/or perform instructedactions for, the electronic device 102 and/or the electronic sensordevices 106A-C. One or more of the electronic devices 112A-C may includeone or more sensors and/or sensor devices, such as one or more of thesensor devices 106A-C, for sensing, observing, and/or obtaininginformation. The electronic device 112A is depicted in FIG. 1 as arefrigerator device, the electronic device 112B is depicted as a set-topbox device that is communicatively coupled to an output device 114, suchas a display, and the electronic device 112C is depicted as a thermostatdevice. However, the electronic devices 112A-C may be any devices thatprovide information to, and/or perform instructed actions for, theelectronic device 102 and/or the electronic sensor devices 106A-C.

In one or more implementations, the electronic device 102 and/or one ormore of the electronic sensor devices 106A-C includes an output device,such as a display and/or speakers, for providing context-awarebehavioral feedback to a user. The electronic device 102 and/or one ormore of the electronic sensor devices 106A-C may be configured toaccess, and/or provide context-aware behavioral feedback via, an outputdevice associated with a communicatively coupled device, such as theoutput device 114 of the electronic device 112B.

The service provider server 110 may be one or more computing devicesthat that may operate in conjunction with the electronic device 102,and/or one or more of the electronic sensor devices 106A-C, to providethe context-aware decision making system. For example, the serviceprovider server 110 may include one or more server devices and adatabase that securely stores user biometric information, profiles,models, etc., e.g. in lieu of and/or in addition to, the electronicdevice 102. One or more of the electronic device 102, the electronicsensor devices 106A-C, the service provider server 110, and/or theelectronic devices 112A-C, may be, and/or may include all or part of,the electronic system illustrated in FIG. 7.

The network 108 may include a gateway device, such as the electronicdevice 112B, that facilitates communications of the electronic device102, the electronic sensor devices 106A-C, the service provider server110, and/or the electronic devices 112A-C. The electronic device 102 mayoperate as, and/or in conjunction with, the gateway device to facilitateproviding a context-aware decision making system. The electronic device112B may host at least a portion of the context-aware decision makingsystem, e.g. in lieu of and/or in addition to the service providerserver 110.

The electronic device 102 and/or the service provider server 110 maycollect information over a period of time, such as via one or more ofthe electronic sensor devices 106A-C, that pertains to a user, such asone or more biometric data items that collectively form one or morebiometric profiles of the user, information that describes behaviorsand/or activities of the user, such as world interactions, sleeping,relaxing, working, stress activities, calm activities, and/or one ormore environmental variables that form one or more environmentalprofiles, such as time of day, location of the user, weatherinformation, information regarding devices proximal to the user, etc.The environmental variables and/or environmental profiles may beassociated with one or more of the biometric profiles and/or theinformation describing the behaviors of the user. For example, theenvironmental profiles may describe environments in which the user islocated when the biometric data items and/or the information describingthe behaviors of the user is collected.

The aforementioned biometric profiles of the user may include one ormore biometric data items collected instantaneously, and/or over aperiod of time, for the user, such as one or more biometric data itemscollected from the same and/or different electronic sensor devices106A-C over a period of time. A biometric profile may include, and/ordescribe, a biological rhythm of the user. In one or moreimplementations, one or more of the biometric data items are biometricvalues measured from the user by one or more of the electronic sensordevices 106A-C, such as heart rate. One or more of the biometric dataitems may relate to moods of the user that are empirically measurableand/or observable by one or more of the electronic sensor devices106A-C, such as a happy mood, a sad mood, etc. One or more of thebiometric data items may relate to responses of the user that areempirically measurable and/or observable by the electronic sensordevices 106A-C, such as responses to one or more stimuli that may bedescribed in an associated environmental profile.

In one or more implementations, the empirically determinable and/orobservable moods and/or responses that relate to the biometric dataitems includes one or more pleasure moods/responses determinable fromfacial expressions, such as smiling, pleasure moods/responsesdeterminable from verbal expressions, such as “ummm,” and/or pleasuremoods/responses determinable from any measurable biometric values. Theempirically determinable and/or observable moods and/or responses mayinclude one or more displeasure moods/responses determinable from facialexpressions, such as frowning, displeasure moods/responses determinablefrom verbal expressions, such as “yuck,” and/or displeasuremoods/responses determinable from any measurable biometric values.

The aforementioned environmental profiles of the user may include one ormore environmental variables collected instantaneously, and/or over aperiod of time, with respect to the user, such as a descriptive and/ordiscrete location of the user, a time of day associated with the user,information regarding clothes being worn by the user and/or activitiesbeing engaged in by the user, a current weather associated with theuser, information regarding one or more devices that are proximal to theuser, or generally any environmental variables that describe and/orrelate to an environment associated with the user. The descriptiveand/or discrete location of the user may describe the environmentassociated with the user and/or the environment in which the user islocated, such as in a kitchen, in a car, in a bedroom, etc.

The electronic device 102, the service provider server 110, and/or oneor more of the electronic sensor devices 106A-C may continuously monitorand/or update a current biometric profile of the user and/or a currentenvironmental profile of the user, irrespective of whether any requestshave been received therefor. For example, the electronic device 102and/or the service provider server 110 may continuously receive datafrom one or more of the electronic sensor devices 106A-C and/or theelectronic devices 112A-C, and continuously update the current biometricprofile of the user and/or the current environmental profile of the userwith the received data.

The electronic device 102 and/or the service provider server 110 may usethe collected information and/or profiles associated with the user,which is referred to as historical data, to predict behaviors of theuser in order to provide context-aware behavioral feedback to the user,e.g. information that the user is interested in and/or actions thatfacilitate the user in performing the predicted behaviors. In one ormore implementations, the electronic device 102, and/or the serviceprovider server 110, generates one or more predictive models that areused to predict the behaviors of the user, such as based at least on thecurrent environment in which the user is located. For example, when theuser walks into a room, the electronic device 102 and/or the serviceprovider server 110 determines context-aware behavioral feedback that isuseful to the user, such as based at least on a predicted behavior ofthe user in the room, and/or actions that facilitate the user withperforming a predicted behavior in the room.

The context-aware behavioral feedback provided by the electronic device102 and/or the service provider server 110 may take many differentforms. The feedback may be information provided for display to a user,the feedback may be used to perform an action on behalf of the user,initiate performance of an action on behalf of the user, reconfigure anapplication on behalf of a user, make a software selection on behalf ofthe user, such as based on the user's preferences and/or biometricprofile, and/or the feedback may be any information that may be providedto the user and/or used to change, effect, or perform an action onbehalf of the user.

In one or more implementations, if the user walks towards a picturewindow in the morning, and the user typically views weather informationin the morning, the electronic device 102 predicts this user behaviorbefore it occurs and provides a heads up display, such as via electronicsensor device 106B, and/or verbal communication of, the temperatureand/or weather forecast for the day, such as retrieved from an onlineweather portal. The electronic device 102 may provide a suggestion tothe user based at least on the weather forecast, such as to avoidbarbeques when there is rain in the forecast.

If the user opens a refrigerator in the morning, such as the electronicdevice 112A, the electronic device 102 predicts this user behaviorbefore it occurs and suggests to the user what is most likely to bepleasing for the user to cook/eat for breakfast, such as based at leaston historical breakfast eating habits of the user, available food in therefrigerator (e.g., as provided by the electronic device 112A), and/orhistorical pleasurable and/or unpleasurable responses for recipes thatcan be made based at least on the available food. The recipes may beprovided by a recipe portal, such as a portal maintained by the serviceprovider server 110.

In one or more implementations, when the electronic device 102 predictsthat the user will and/or should eat soon, the electronic device 102suggests food for the user to cook/eat based at least on diet offeringsfor the user and/or perishable dates of the available food. Therefrigerator device, such as the electronic device 112A, and/or acupboard device, may maintain catalogs of available food, such as byusing an imager, radio frequency identification technology, and thelike, and provides an indication of the available foods to theelectronic device 102, such as when the electronic device 102 predictsthat the user will and/or should eat soon. The electronic sensor device106B may include a camera device that recognizes foods in therefrigerator device and/or reads and/or recognizes ingredients ofrecipes being prepared by the user, and the electronic sensor device106B provides an indication of the recognized foods and/or ingredientsto the electronic device 102. The electronic device 102 may operate in afull mode where a recipe is only suggested to the user when the fullrecipe can be met based at least on available food, and/or in a partialmode where a recipe is suggested to the user when one or moreingredients are missing and/or can be substituted with another availableingredient.

The electronic device 102 may filter the context-aware behavioralfeedback provided to the user based at least on recent behaviors of theuser, such as a recent activity level of the user. For example, if theuser did not exercise recently, the electronic device 102 suggests thatthe user skip desert when the user is looking at ice cream. Theelectronic device 102 and/or the electronic sensor device 106B may useoptical processing so that the ice cream is not visible to the user,and/or so that the ice cream looks like some other food thathistorically causes an unpleasurable response from the user. Thus, theelectronic device 102 and/or the service provider server 110 identifiesand store historical behaviors of the user (and/or other users) for agiven environment, such as described by the location, time of day, etc.,and uses the stored information to predict behaviors in a similarenvironment in the future and provide context-aware behavioral feedbackassociated therewith. An example process of providing context-awarebehavioral feedback based at least on predicted user behaviors isdiscussed further below with respect to FIG. 2.

The electronic device 102 may allow a user to select a behavioral policythat is intended to guide the user's behaviors, such as a dietingbehavioral policy, that the user would like to adhere to. The electronicdevice 102 determines suggested behaviors for the user based at least onthe behavioral policy. When providing context-aware behavioral feedbackto the user, the electronic device 102 performs actions to facilitatethe user with conforming predicted behaviors to the suggested behaviorsdetermined from the behavioral policy. The electronic device 102 maycollect data from the electronic sensor devices 106A-C, evaluate thecollected data to characterize one or more behaviors as unhealthy, andprovides context-aware behavioral feedback by attempting to modify thebehaviors that are characterized as unhealthy, such as by facilitatingthe user with conforming the unhealthy behaviors to behaviors indicatedby the dieting behavioral policy.

In one or more implementations, the electronic device 102 providescontext-aware behavioral feedback to the user in the form of suggestionsfor improving monitored user behaviors, such as sleeping, eating,exercising, etc. For example, the electronic device 102 providessuggestions regarding the amount of time the user is sleeping, theamount of time the user is seated, the amount of exercising the user hasdone, such as walking, running, etc., the posture of the user, theamount of healthy foods, such fruits and/or vegetables, consumed by theuser, the alertness of the user, etc. The electronic device 102 maydownload data, such as from the service provider server 110, forresponding to recently sensed behaviors, such as playing relaxing musicwhen the user is tired and/or has not slept enough, and/or playing analarm when the user is tired but not in an environment for sleeping. Theelectronic device 102 provides context-aware behavioral feedback to theuser for conforming the predicted behavior of the user to the suggestedbehavior of the user by adding subliminal visual frames, such as via theelectronic sensor device 106B, to encourage and/or cause the user todislike unhealthy foods, such as sweets or food in general, and likehealthy foods, such as salads. Similar context-aware behavioral feedbackprovided by the electronic device 102 includes applying a filter tosearch engine results to identify and/or isolate healthy search results,such as color coding low fat restaurants on a map of search results,and/or removing other restaurants from the map of the search results.

The electronic device 102 may take into account what has been consumedby the user already in a given day and provides context-aware behavioralfeedback to facilitate the user with maintaining a balanced calorieand/or vitamin intake for the day. For example, if the electronic device102 determines that the user ate unhealthy food for breakfast, and thatthe user is hungry for lunch, the electronic device 102 provides healthylunch suggestions to the user, and/or lunch suggestions that requiresubstantial walking distance to counter additional calorie consumption.If the electronic device 102 determines that the user ate healthy foodfor breakfast, the electronic device 102 may reward the user with abroader selection of options for lunch and/or suggest options thatinclude eating at home and/or driving. The electronic device 102 maymonitor the amount and/or type of food already consumed by the user withfacilitation of one or more of the electronic sensor devices 106A-C. Forexample, the electronic device 102 may request that the user identifytheir hand size, and the electronic device 102 uses the hand size to mapa volume of food consumed, such as determined from a camera device ofone or more of the electronic sensor devices 106A-C. The electronicdevice 102 can also identify the number of calories consumed by the userbased at least on image matching, voice ordering recognition mapped torestaurant caloric content, box caloric label information (whencooking), etc.

In one or more implementations, the electronic device 102 providescontext-aware behavioral feedback to the user in the form of a dailycalorie meter and/or a balanced diet meter, such as via an output deviceof the electronic sensor device 106B. If the electronic device 102detects that the user consumed a certain number of calories, then theelectronic device 102 causes the meter to rise, and if the electronicdevice 102 determines that the user has exercised, then the electronicdevice 102 causes the meter to slowly fall, such as proportional to thenumber of calories that the user burned by exercising. The electronicdevice 102 may also provide a current weight, a goal weight, and/orinformation regarding long term behaviors, such as percentage ofcalories breaking the behavioral policy, and that such breaks have acumulative total calories at some certain level of pounds. Theelectronic device 102 may perform a metabolism analysis of the userand/or the electronic device 102 provides a doctor interface to the userfor doctor guidance and/or feedback via a portal. If the user is notexercising enough the electronic device 102 interfaces with a socialnetworking engine to identify similarly situated other users and addthem to the user's walking, running, etc., groups, such as to encouragethe user to exercise more.

The electronic device 102 may determine a predicted and/or expectedbiometric profile for the user, based at least on an environmentalprofile associated with the user, such as an environmental profile thatindicates that the user is driving a car. The electronic device 102 thenobtains a current biometric profile for the user and determines whetherthe current biometric profile conforms to the predicted biometricprofile. If the current biometric profile does not conform to thepredicted biometric profile, the electronic device 102 providescontext-aware behavioral feedback by performing an action to facilitatethe user with conforming the current biometric profile to the predictedbiometric profile. The action may be determined based at least onactions that have historically conformed the user's current biometricprofile to the predicted biometric profile.

The electronic device 102 may attempt to associate behaviors and/oractivities of the user with data received from one or more of theelectronic sensor devices 106A-C, such as biometric data. Then, in viewof unhealthy or bothersome biorhythmic indications, e.g. as indicated bythe current biometric profile of the user, the electronic device 102provides context-aware behavioral feedback by suggesting activitiesand/or behaviors (e.g. via advertising) that have previously provensuccessful for adjusting the current biometric profile of the user toconform to the predicted biometric profile.

The electronic device 102 may collect biometric profiles of a user andassociated behaviors and/or environmental profiles to identify theeffect that behaviors and/or activities have on the biometric profilesof the user, such as the effects of petting a dog, watching a boring oradventure movie, listening to a particular artist, eating a particularfood, drinking coffee, talking to a particular person, etc. Thebehaviors and/or activities do not need to be specifically identified todetermine the effect that they have on the biometric profile of theuser. Thus, the electronic device 102 can provide context-awarebehavioral feedback in the form of suggested behaviors and/or activitiesto coax the user away from some activities and thoughts, and towardothers, such as to appropriately balance the current biometric profileof the user. Since users may differ, and a stimulus that makes one userhappy may make another user angry, the performed actions and/orsuggestions can be personalized for the particular user. An exampleprocess for performing actions to conform a current biometric profile ofa user to an expected biometric profile is discussed further below withrespect to FIG. 3. An example data flow of a system for performingactions to conform a current biometric profile of a user to an expectedbiometric profile is discussed further below with respect to FIG. 5.

The electronic device 102 may determine a predicted behavior of the userthat is unsafe, such as based at least on a current environmentalprofile of the user, and the electronic device 102 providescontext-aware behavioral feedback by attempting to correct the behaviorbefore it is preformed and/or completed by the user. The electronicdevice 102 may identify dangerous environments, such as dangerousdriving conditions, and provides context-aware behavioral feedback byperforming an action when the biometric profile of the user does notconform to a predicted and/or expected biometric profile for theparticular environment.

The electronic sensor device 106B may employ pattern and/or movementrecognition to identify factory and/or home environments that are likelyto result in injury, and provides an environmental profile indicatingthe same to the electronic device 102. The electronic device 102 mayprovide context-aware behavioral feedback by warning the user, andotherwise educates the user in an attempt at injury avoidance. Thus, theelectronic device 102 uses context-awareness to identify dangerousenvironments, and provides context-aware behavioral feedback by alertingthe user and/or attempting to modify the behavior of the user while inthe dangerous environment.

The electronic device 102 may use geolocation information to determinedangerous environments, such as dangerous intersections where the usershould be particularly alert, and/or weather information can be used todetermine poor weather conditions when the user should be particularlyalert. The electronic device 102 then provides context-aware behavioralfeedback to alert the user to avoid hazards detected by the electronicdevice 102 and/or the electronic sensor devices 106A-C. In one or moreimplementations, the electronic sensor devices 106A-C determine when theuser is likely to fall asleep, e.g. based at least on a currentbiometric profile of the user, recent sleep characteristics of the user,past sleep at the wheel events of the user, and/or and othersusceptibilities. Thus, the electronic device 102 may predict that theuser will fall asleep within a certain driving time on a certain type ofroad (highway), before the behavior actually occurs, and the electronicdevice 102 attempts to prevent and/or impede the predicted behavior fromoccurring. The electronic device 102 may also receive information from avehicle of the user and/or one or more devices associated therewith,such as quick steering corrections and braking data currently and in thepast. The electronic device 102 may use the information received fromthe vehicle to predict and/or measure a tired driving behavior of theuser.

The electronic device 102 may perform one or more actions to providecontext-aware behavioral feedback in response to detecting the expectedsleeping at the wheel behavior, such as warn the user when a route isprogrammed, changing the route to one that requires more constantattention, selecting music for the user, suggesting a coffee shop stop,suggesting a motel, jolting the user, or otherwise waking up the userwhen the user is driving. The electronic device 102 may incorporate theamount of alcohol consumed by the user and/or medications taken by theuser when predicting the sleep behavior. In one or more implementations,bad driving behaviors by the user (predicted behaviors and/or currentbehaviors that are beyond the norm for the user and/or outside of asafety level), in addition to a current biometric profile, recent useractivity history, and/or long term related history, can be used to forcethe user to pull over and/or to prevent the user's vehicle from driving.The electronic device 102 may gather data regarding sleep patterns ofthe user generally, and/or associated biometric profile information ofthe user, and the electronic device 102 compares how the user (and/orother users) performed various activities under similar sleep patternsand/or biometric profiles, such as from a safety standpoint. An exampleprocess of conforming a user's biometric profile to a safe biometricprofile for a given environment is discussed further below with respectto FIG. 3.

The electronic device 102 may use the current biometric profile of theuser to provide context-aware behavioral feedback with respect to onlinebehaviors of the user, such as online searching. The electronic device102 may implement context-aware behavioral feedback to online searchingby using the current biometric profile of the user as input to a searchor selection algorithm. For example, the current biometric profile ofthe user and/or an associated environmental profile can be used as asearch input, and/or to supplement or modify search input. In one ormore implementations, the biometric profile of the user indicates arelaxed state and prone position of the user, the environmental profileindicates that the user is at home late in the day on Saturday, and as aresult the electronic device 102 initiates playing music having anupbeat tempo, such as to encourage the user to exercise. If theenvironmental profile indicates that it is night time, the electronicdevice 102 initiates the playing of soothing music, and/or initiatesstopping the music when the current biometric profile of the userindicates that the user is falling asleep.

The electronic device 102 may receive a search from the user for musicby a particular band, and the electronic device 102 considers thecurrent biometric profile and/or a current environmental profile of theuser, to provide context-aware behavioral feedback by ranking songs bythe band in different orders. For example, the songs that havehistorically proven (by the user and/or by others similar users) tocause surges in biometric profiles might be offered higher on the searchresults list in the morning than late at night on a work day. Thesupplemented searching may be applied to any systems that include asearch and/or user preference feature, such as any Internet searchplatforms. An example data flow of a system for providing context-awarebehavioral feedback through biometric profile and/or environmentalprofile supplemented searching is discussed further below with respectto FIG. 6.

In one or more implementations, the network environment 100 may includeadditional users, such as houseguests, co-workers, etc., and at leastone of the additional users may be associated with at least oneadditional electronic sensor device 106A-C and/or at least oneelectronic device 102. The electronic device 102 of the user may receivecurrent biometric profiles of the additional users, expected/predictedbiometric profiles of the additional users, and/or preferences of theadditional users, such as musical preferences, food preferences, etc.,via the electronic sensor devices and/or electronic devices of theadditional users. The preferences of the additional users may be defaultpreferences and/or predicted preferences. The electronic device 102 mayprovide the preferences and/or profiles for display to the user, such asvia a screen of the electronic device 102 and/or via an output deviceassociated with the electronic sensor device 106B. The preferences maynot be provided for display to the additional users.

The preferences may be genres, such as spicy food, or classical music,and/or the preferences may be specific items, such as specific foods oneof the additional users likes and/or has eaten in the past month, or amusic playlist of one of the additional users. The user of theelectronic device 102 views the preferences of the additional users, andmakes selections accordingly. The preferences may include negativepreferences, such as dislikes and/or foods that an additional user isallergic to. The electronic device 102 may provide the preferences ofall of the additional users for display and/or selection, and/or theelectronic device 102 may filter the preferences to only display groupconsensus items as possible selections, such as for music.

In one or more implementations, the electronic device 102 providescontext-aware behavioral feedback for the users collectively byautomatically making selections and/or controlling one or more of theelectronic devices 112A-C, based at least on the received preferences,current biometric profiles, predicted biometric profiles and/or otherdata received from the devices of the additional users. The electronicdevice 102 may predict preferences of the additional users based atleast on the current and/or predicted biometric profiles of theadditional users, such as by sensing activities, moods, biometricprofiles, etc. of the additional users. The electronic device 102 mayreceive other information from the electronic devices associated withthe additional users that the electronic devices determines to beuseful. For example, recent re-listening to a particular song by anadditional user results in a preference for such song being provided tothe electronic device 102, such as for consideration in selecting totrigger background music and playing the song.

The electronic device 102 may also receive immediate context-awarebehavioral feedback from the electronic devices and/or electronic sensordevices of the additional users that indicates whether the additionalusers are enjoying a current selection, such as a current musicselection, and the electronic device 102 adjusts the music selection inreal time based at least on the feedback. The electronic device 102 mayreceive updated current biometric profiles of the additional users thatthe electronic device 102 compares to previously received biometricprofiles of the additional users to determine the effect of the currentselection on the additional users. Thus, the electronic device 102 mayreceive non-verbal feedback that is used to impact the selections madeby the user and/or the electronic device 102.

In one or more implementations, the electronic device 102 gathers andstore time-related data that includes bio-sensing data, such asbiometric data, user interaction data, and/or software application data.The electronic device 102 may process the time-related data to identifybio-impacting relationships, such as between the bio-sensing data andthe user interaction data and/or software application data. Theelectronic device 102 may identify current biometric data of a user,such as from the gathered bio-sensing data, and the electronic device102 responds to the identification by performing an action that attemptsto alter future bio-sensing data of the user.

In one or more implementations, the electronic device 102 stores firstdata with a timing relationship. The first data may include bio-sensingrelated data and/or software application related data that is gatheredover a period of time. The electronic device 102 processes the firstdata to identify software application related data that has abio-impact, such as software application related data that impacts thebio-sensing related data. The electronic device 102 performs an actionto a cause a future generation of the software application related datato cause the first bio-impact, such as in order to facilitate conforminga current biometric profile of the user to an expected biometric profileof the user.

In one or more implementations, the electronic device 102 may storegathered data with a timing relationship. The gathered data may includebio-sensing related data and device operational data that is collectedover time. The electronic device 102 processes the gathered data toidentify a predictive association between a first portion of thebio-sensing related data, first portion of the device operational data,and/or non-bio-sensing context information. The electronic device 102performs an action to cause a future generation of the deviceoperational data in response to obtaining future counterparts of thefirst portion of the bio-sensing related data. The action includessending a communication to one or more remote devices, providing anoffer to a user, performing a software reconfiguration, and/orperforming a software selection. The gathered data may originate fromwithin the electronic device 102 and/or from one or more environmentaldevices.

FIG. 2 illustrates a flow diagram of an example process 200 of acontext-aware decision making system in accordance with one or moreimplementations. For explanatory purposes, the example process 200 isprimarily described herein with reference to electronic device 102 ofFIG. 1; however, the example process 200 is not limited to theelectronic device 102 of FIG. 1, and the example process 200 may beperformed by one or more components of the electronic device 102. In oneor more implementations, all or part of the example process 200 may beperformed by the service provider server 110, by one or more of theelectronic sensor devices 106A-C, and/or by the electronic device 102.Further for explanatory purposes, the blocks of the example process 200are described herein as occurring in serial, or linearly. However,multiple blocks of the example process 200 may occur in parallel. Inaddition, the blocks of the example process 200 may be performed adifferent order than the order shown and/or one or more of the blocks ofthe example process 200 may not be performed.

The electronic device 102 may implement the example process 200 togenerate a user behavioral predictive model to predict a behavior of auser in a current environment and then provide context-aware behavioralfeedback to the user by providing information related to the predictedbehavior of the user, performing an action to facilitate the predictedbehavior of the user, and/or performing an action to facilitateconforming the predicted behavior of the user to a suggested behavior,such as a suggested behavior determined from a behavioral policy.

The electronic device 102 generates the user behavioral predictive modelbased at least in part on historical behaviors of the user and/orassociated environmental profiles of the user at the time that thehistorical behaviors were performed (202). In one or moreimplementations, the electronic device 102 performs one or more featureselection algorithms to determine one or more environmental variables ofthe environmental profiles to use as features for the predictive model,such as the environmental variables that are most indicative of thecorresponding historical behaviors. The electronic device 102 may use analgorithm, such as a k-nearest neighbor algorithm, to generate thepredictive model using at least the determined features and theassociated historical behaviors. The electronic device 102 may receivethe user behavioral predictive model, e.g. from the service providerserver 110. The electronic device 102 may continuously retrain, oradjust, the user behavioral predictive model as additional behaviors ofthe user are observed.

The electronic device 102 receives data from one or more of theelectronic sensor devices 106A-B, and/or from the electronic devices112A-C, from which the current environment of the user is obtainableand/or detectable (204). The electronic device 102 may receive the datafrom a sensor of the electronic device 102, such as a positioningsensor, an accelerometer, etc. The electronic device 102 obtains and/ordetects the current environmental profile of using at least the receiveddata (206). For example, the current environmental profile may indicatethat the user is at their home, standing in their kitchen, looking intheir refrigerator on a Sunday morning.

The electronic device 102 determines a predicted behavior of the user byapplying the current environmental profile to the user behavioralpredictive model (208). For example, the electronic device 102determines a predicted behavior of eating breakfast. The electronicdevice 102 determines whether a suggested behavior can be determined(210). For example, the electronic device 102 determines whether abehavioral policy was received for the user (212). The behavioral policyindicates behaviors suggested for the user to maintain an associatedlifestyle, such as a diet behavioral policy.

If a behavioral policy was received for the user (212), the electronicdevice 102 determines a suggested behavior based at least on thebehavioral policy (224). In one or more implementations, the suggestedbehavior is determined based at least on the behavioral policy andrecent behaviors of the user. For example, the behavioral policy allowsthe user to consume a total number of calories per day and the suggestedbehavior is based at least on the number of calories the user hasalready consumed for the day relative to the total number of caloriesallowed. The electronic device 102 then performs an action to facilitateconforming the predicted behavior to the suggested behavior (230). Forexample, the electronic device 102 suggests that the user not prepare ameal or eat any foods if the user has recently consumed a large numberof calories, or that the user eat a higher calorie meal if the user hasnot consumed many calories that day.

If the electronic device 102 did not receive a behavioral policy for theuser (212), the electronic device 102 obtains the current biometricprofile of the user (226). For example, the electronic device 102receives one or more biometric data items from the electronic sensordevices 106A-C, and determines the biometric profile of the user basedat least in part on the received biometric data items. The electronicdevice 102 then determines the suggested behavior for the user based atleast in part on the current environmental profile and the currentbiometric profile of the user (228). For example, the electronic device102 applies the current biometric profile of the user and/or the currentenvironmental profile to another predictive model that indicates thebehaviors and/or activities that other users have found preferable inthe past when the other users had similar biometric profiles as thecurrent biometric profile of the user and/or when the other users (suchas healthy users) were in a similar environment as the environmentdescribed by the current environmental profile of the user. The anotherpredictive model may be provided by, for example, a doctor, a dietaryexpert, a governmental agency, such as the FDA, or the like. Theelectronic device 102 then performs an action to facilitate conformingthe predicted behavior of the user to the suggested behavior (230).

If the electronic device 102 determines that a suggested behavior cannotbe determined (210), the electronic device 102 determines whether anaction can be performed that facilitates the predicted behavior (214).For example, the electronic device 102 determines whether it can performan action that facilitates the predicted behavior and/or whether theelectronic device 102 can instruct any other electronic devices 112A-Cto perform an action that facilitates the predicted behavior. If theelectronic device 102 determines that an action can be performed thatfacilitates the predicted behavior (214), the electronic device 102performs the action to facilitate the predicted behavior (232). Forexample, if the electronic device 102 predicts that the user willprepare a meal that requires an oven device to be pre-heated to aparticular temperature, the electronic device 102 initiates preheatingthe oven device to the particular temperature, such as by transmittingan instruction to the oven device.

If the electronic device 102 determines that there are no actions thatcan be performed to facilitate the predicted behavior (214), theelectronic device 102 determines and/or retrieve information related tothe predicted behavior (216). For example, the electronic device 102retrieves information regarding foods that are available in arefrigerator device. The electronic device 102 then receives currentbiometric data of the user, such as from one or more of the electronicsensor devices 106A-C (218).

The electronic device 102 filters the information based at least on thecurrent biometric data of the user and/or recent behaviors of the user(220). For example, if the biometric data of the user indicates that theuser is thirsty, and/or the recent behavior of the user indicates thatthe user was exercising, the electronic device 102 filters theinformation to only include information regarding sports drinks that areavailable in the refrigerator. The electronic device 102 then providesthe filtered information to the user (222), such as via a display, viathe electronic sensor device 106B, and/or via an output of therefrigerator device. In one or more implementations, the electronicdevice 102 provides the information to the user (222) without filteringthe information. The electronic device 102 may provide informationrelated to a predicted behavior (222), in addition to performing anaction that facilitates the predicted behavior (232).

FIG. 3 illustrates a flow diagram of an example process 300 of acontext-aware decision making system in accordance with one or moreimplementations. For explanatory purposes, the example process 300 isprimarily described herein with reference to electronic device 102 ofFIG. 1; however, the example process 300 is not limited to theelectronic device 102 of FIG. 1, and the example process 300 may beperformed by one or more components of the electronic device 102. In oneor more implementations, all or part of the example process 300 may beperformed by the service provider server 110, by one or more of theelectronic sensor devices 106A-C, and/or by the electronic device 102.Further for explanatory purposes, the blocks of the example process 300are described herein as occurring in serial, or linearly. However,multiple blocks of the example process 300 may occur in parallel. Inaddition, the blocks of the example process 300 may be performed adifferent order than the order shown and/or one or more of the blocks ofthe example process 300 may not be performed.

In one or more implementations, the electronic device 102 implements theexample process 300 to generate a user behavioral predictive model todetermine an expected biometric profile of the user in a currentenvironment and then provide context-aware behavioral feedback to theuser by performing an action that facilitates the user with maintainingtheir current biometric profile (when their current biometric profilesubstantially coincides with the expected biometric profile of the userfor the current environment) or that facilitates the user withconforming their current biometric profile to the expected biometricprofile of the user for the current environment.

The electronic device 102 generates the user behavioral predictive modelbased at least in part on historical biometric profiles of the userand/or associated environmental profiles at the time that the historicalbiometric profiles were collected (302). The user behavioral predictivemodel may be based at least on the historical biometric profiles ofother users and the associated environmental profiles of the otherusers. The user behavioral predictive model may be based at least onrecommended biometric profiles for users for given environmentalprofiles, such as the biometric profiles indicated by a manufacturer ofa vehicle, and/or biometric profiles set by a regulating and/orgovernmental agency.

The electronic device 102 obtains a current environmental profile thatindicates a current environment of the user, such as based at least ondata received from one or more of the electronic sensor devices 106A-Band/or from one or more of the electronic devices 112A-C (304). Theelectronic device 102 determines an expected biometric profile of theuser based at least on the user behavior predictive model and/or thecurrent environmental profile (306). For example, the electronic device102 applies the current environmental profile of the user to the userbehavioral predictive model to determine an expected biometric profileof the user (308). The expected biometric profile may indicate thetypical biometric profile of the user for the environment, a recommendedbiometric profile of the user for the environment, and/or a minimumbiometric profile of the user for the environment, such as indicated bya governmental agency. For example, if the environmental profileindicates that the user is driving in an identified type of car, on anidentified road, at an identified time of day, and in identified weatherand/or traffic conditions, the expected biometric profile may indicatethe minimum biometric profile for operating the identified type of caron the identified road at the identified time of day and in theidentified weather and/or traffic conditions. If the expected biometricprofile is not based at least on the typical biometric profile of theuser, the expected biometric profile may be adjusted based at least onthe typical biometric profile of the user, such as to account forbiometric particularities of the user.

The electronic device 102 determines whether the current biometricprofile of the user differs from the expected biometric profile for theuser (310), such as by more than a threshold amount. If the currentbiometric profile does not differ from the expected biometric profile(310), such as by more than the threshold amount, the electronic device102 performs an action that facilitates maintaining the currentbiometric profile of the user (312). For example, the electronic device102 selects a next music track and/or station that is similar, e.g.consistent tempo, etc., with music currently being listened to by theuser.

If the electronic device 102 determines that the current biometricprofile of the user differs from the expected biometric profile of theuser (310), such as by more than the threshold amount, the electronicdevice 102 determines a biometric profile adjustment for conforming thecurrent biometric profile to the expected biometric profile (314). Forexample, if the current biometric profile of the user indicates that theheart rate of the user is below the expected heart rate for the user forthe current environment, the biometric profile adjustment indicates thatthe heart rate of the user should be elevated to a level consistent withthe expected biometric profile.

The electronic device 102 determines a biometric profile of the userthat achieves the biometric profile adjustment (316). For example, ifthe heart rate of the user needs to be elevated to a particular level,the electronic device 102 identifies a biometric profile for which theheart rate of the user is at or above the particular level. Theelectronic device 102 determines a user behavior that is mapped to thebiometric profile that achieves the biometric profile adjustment (318).For example, the electronic device 102 identifies historical userbehaviors that are mapped to the biometric profile, such as certainmusical selections that have previously achieved the biometric profile,and/or the electronic device 102 utilizes a predictive model to identifybehaviors that are associated with the biometric profile. The electronicdevice 102 then performs an action that facilitates the user behaviorthat is mapped to the biometric profile that achieves the biometricprofile adjustment (320).

FIG. 4 illustrates a flow diagram of an example process 400 of acontext-aware decision making system in accordance with one or moreimplementations. For explanatory purposes, the example process 400 isprimarily described herein with reference to electronic device 102 ofFIG. 1; however, the example process 400 is not limited to theelectronic device 102 of FIG. 1, and the example process 400 may beperformed by one or more components of the electronic device 102. In oneor more implementations, all or part of the example process 400 may beperformed by the service provider server 110, by one or more of theelectronic sensor devices 106A-C, and/or by the electronic device 102.Further for explanatory purposes, the blocks of the example process 400are described herein as occurring in serial, or linearly. However,multiple blocks of the example process 400 may occur in parallel. Inaddition, the blocks of the example process 400 may be performed adifferent order than the order shown and/or one or more of the blocks ofthe example process 400 may not be performed.

In one or more implementations, the electronic device 102 implements theexample process 400 to generate a user behavioral predictive model topredict a behavior of a user based on the location and/or currentbiometric profile of the user, and then provide context-aware behavioralfeedback to the user by providing information related to the predictedbehavior of the user where the information is filtered based at least ona current environment of the user.

The electronic device 102 receives historical user behavior informationthat describes historical user behaviors, and associated biometric dataitems and location information that were collected at or near the timethat the historical behaviors were performed (402). The electronicdevice 102 generates a user behavioral predictive model using at leastthe historical user behavior information, the associated biometric dataitems and the associated location information (404). The electronicdevice 102 collects current biometric data items from the user, such asvia the electronic sensor devices 106A-C (406). The electronic device102 determines whether current location information associated with theuser is determinable (408), such as from data received from one or moreof the electronic sensor devices 106A-C.

If the current location information associated with the user isdeterminable (408), the electronic device 102 applies the currentbiometric data and current location information to the predictive modelto determine a predicted user behavior (412). If the current locationinformation associated with the user cannot be determined (408), theelectronic device 102 applies the current biometric data to thepredictive model (without any location information) to determine apredicted user behavior (410).

The electronic device 102 determines a set of information based at leastin part on the predicted user behavior (414). For example, if thepredicted behavior is that the user will listen to music, the electronicdevice 102 determines the songs that are available for the user tolisten to. The electronic device 102 obtains at least one environmentalvariable, such as time of day, recent user behaviors, etc. (416). Theelectronic device 102 filters the information based at least in part onthe environmental variable (418). For example, the electronic device 102filters songs that the user recently listened to and/or the electronicdevice 102 filters songs based at least on the time of day. Theelectronic device 102 then provides the filtered information to theuser, such as via a screen on the electronic device 102 and/or via anoutput device of another device, such as the electronic sensor device106B (420).

FIG. 5 illustrates an example data flow 500 of a context-aware decisionmaking system in accordance with one or more implementations. Not all ofthe depicted components may be used, however, and one or moreimplementations may include additional components not shown in thefigure. Variations in the arrangement and type of the components may bemade without departing from the spirit or scope of the claims as setforth herein. Additional components, different components, or fewercomponents may be provided.

The data flow 500 includes data and communications that may betransmitted between the electronic device 102 and the service providerserver 110 to provide context aware behavioral feedback to a user. Thedata flow 500 further illustrates components of the electronic device102 and the service provider server 110 that facilitate providing thecontext-aware behavioral feedback to the user.

In this regard, the electronic device 102 includes a context datacollection and management module 502, a biometric data collectionelements and management module 503, a remote analysis support module504, user interfacing elements 508, an adaptive biometric predictivesuggestion, command generation, and management module 505, and a localbiometric profiling support database 501. The adaptive biometricpredictive suggestion, command generation, and management module 505includes a local initial and performance analysis module 506A, and alocal and remote command/suggestion processing module 506B. The userinterfacing elements 508 may include one or more applications, operatingsystems, and/or browsers, and may also include a local/remotecommand/suggestion processing module 509. The local biometric profilingsupport database 501 may store local current and historical biometricinformation, local current and historical context information, such asenvironmental profiles, and/or related analysis information. The localbiometric profiling support database 501 provides suggestion/commandmonitoring and performance feedback data 507 to the adaptive biometricpredictive suggestion, command generation, and management module 505.

The service provider server 110 includes a services portalinfrastructure 511 and an online support infrastructure 521. Theservices portal infrastructure 511 includes an adaptive biometricpredictive suggestion and command processing module 512, and a servicesinfrastructure 513. The services infrastructure 513 includes aselectable performance modes, configurations, interactions, andoperations module 514. The online support infrastructure 521 includes anadaptive biometric predictive suggestion, command generation, andmanagement module 522, and a remote biometric profiling support database523. The remote biometric profiling support database 523 may store localcurrent and historical biometric data, local current and historicalcontext information, such as environmental profiles, and/or relatedanalysis information.

In the data flow 500, the electronic device 102 collects biometric dataitems, such as from the electronic sensor devices 106A-C, via thebiometric data collection elements & management module 503. Theelectronic device 102 collects environmental variables and/orenvironmental profiles via the context data collection and managementmodule 502. The electronic device 102 may store the biometric data itemsand/or environmental variables in the local biometric profiling supportdatabase 501. The adaptive biometric predictive suggestion, commandgeneration, and management module 505 then retrieves the storedbiometric data items and/or environmental variables from the localbiometric profiling support database 501, and generates one or morecommands and/or suggestions based at least in part on the retrievedbiometric data items and/or environmental variables. The suggestionsand/or commands may be provided to a user via the user interfacingelements 508. The electronic device 102 may monitor the providedsuggestions, commands, and/or any associated feedback, and provide thesuggestion/command monitoring and performance feedback data 507 to theadaptive biometric predictive suggestion, command generation, andmanagement module 505. The adaptive biometric predictive suggestion,command generation, and management module 505 adjusts the providedsuggestions and/or commands based at least on the suggestion/commandmonitoring and performance feedback data 507.

The electronic device 102 may provide the biometric data items, theenvironmental variables, and/or the suggestion/command monitoring andperformance feedback data 507 to the remote analysis support module 504.The remote analysis support module 504 may provide the biometric dataitems, the environmental variables, and/or the suggestion/commandmonitoring and performance feedback data 507 to the service providerserver 110, such as for storage in the remote biometric profilingsupport database 523. The services portal infrastructure 511 of theservice provider server 110 may receive service requests, such as fromthe electronic device 102, and forwards the service requests to theadaptive biometric predictive suggestion, command generation, andmanagement module 522. The adaptive biometric predictive suggestion,command generation, and management module 522 providescommands-suggestions to the services portal infrastructure 511. Theservices portal infrastructure 511 forwards the commands-suggestions tothe electronic device 102.

FIG. 6 illustrates an example data flow 600 of a context-aware decisionmaking system in accordance with one or more implementations. Not all ofthe depicted components may be used, however, and one or moreimplementations may include additional components not shown in thefigure. Variations in the arrangement and type of the components may bemade without departing from the spirit or scope of the claims as setforth herein. Additional components, different components, or fewercomponents may be provided.

The data flow 600 includes data and communications that may betransmitted between the electronic device 102 and the service providerserver 110 to provide context aware behavioral feedback with respect toonline behaviors of a user, such as online searching. The data flow 600further illustrates components of the electronic device 102 and theservice provider server 110 that facilitate providing context-awarebehavioral feedback with respect to the online behaviors of the user.

In this regard, the electronic device 102 includes a browser/applicationinterface 604, a biometric data and context data collection elements andmanagement module 603, and a memory that stores biometric historicalprofile and user settings data 607A, personal and demographic profiledata 607B, and search history profile data 607C. The browser/applicationinterface 604 includes a supplemental anonymous biometric and profilebased sorting module 605.

As previously discussed with respect to FIG. 1, the service providerserver 110 may include multiple computing devices and/or server devices.With respect to the data flow 600, the service provider server 110includes one or more profile interfaces servers 611, one or more userbiometric profile database servers 612, one or more search interface andbiometric augmenting servers 616, one or more search engine and contentservers 641, and one or more biometric targeted supplemental insertdatabase servers 622. The one or more user biometric profile databaseservers 612 store biometric historical profile and user settings data613A, personal and demographic profile data 613B, and/or search historyprofile data 613C.

In the data flow 600, the electronic device 102 locally stores, such asin a secure element, one or more of the biometric historical profile anduser settings data 607A, the personal and demographic profile data 607B,and/or the search history profile data 607C. The electronic device 102receives search input, such as via the browser/application interface604, and provides the search input, a secure user identifier, and/or acurrent biometric information/code 610 to the service provider server110, such as to the one or more search interface and biometricaugmenting servers 616. In one or more implementations, the currentbiometric information/code is indicative of the current biometricprofile of the user. The one or more search interface and biometricaugmenting servers 616 provide the user identified current biometricinformation/code and/or search input 617 to the one or more userbiometric profile database servers 612.

The one or more user biometric profile database servers 612 determineuser identified biometric augmented data and/or augmented search input615 and provide the user identified biometric augmented data and/oraugmented search input 615 to the one or more search interface andbiometric augmenting servers 616. The user identified biometricaugmented data and/or augmented search input 615 may be provideddirectly to the one or more search engine and content servers 641. Theone or more search interface and biometric augmenting servers 616 usethe user identified biometric augmented data and/or augmented searchinput 615 to generate and provide user identified biometric augmentedsearch input 618 to the one or more search engine and content servers641. The one or more search engine and content servers 641 use the useridentified biometric augmented search input 618 and/or user identifiedbiometric augmented data and/or augmented search input 615 to generateand provide user identified biometric targeted search results 621 backto the one or more search interface and biometric augmenting servers616.

The one or more user biometric profile database servers 612 may provideuser identified biometric profile data and search input 614 to the oneor more biometric targeted supplemental insert database servers 622. Theone or more biometric targeted supplemental insert database servers 622use the user identified biometric profile data and search input 614 togenerate and provide user identified biometric targeted inserts 623,such as advertisements, to the one or more search interface andbiometric augmenting servers 616. The one or more search interface andbiometric augmenting servers 616 use the user identified biometrictargeted search results 621 and/or the user identified biometrictargeted inserts 623 to generate, sort, and/or provide biometrictailored search results with biometric targeted inserts 624 to theelectronic device 102. The electronic device 102 provides the biometrictailored search results with biometric targeted inserts 624 to a user,such as via the browser/application interface 604.

The electronic device 102 may communicate user identified user selectedbiometric settings 609 to the service provider server 110, such as tothe one or more profile interface servers 611. The one or more profileinterface servers 611 stores the user identified user selected biometricsettings 609 in the one or more user biometric profile database servers612. The electronic device 102 may communicate user identified biometricreference/range data 608 to the service provider server 110, such as tothe one or more profile interface servers 611. The one or more profileinterface servers 611 stores the user identified biometricreference/range data 608 in the one or more user biometric profiledatabase servers 612.

FIG. 7 conceptually illustrates an example electronic system 700 withwhich one or more implementations of the subject technology can beimplemented. The electronic system 700, for example, may be, or mayinclude, the electronic device 102, one or more of the electronic sensordevices 106A-C, the service provider server 110, one or more of theelectronic devices 112A-C, a desktop computer, a laptop computer, atablet computer, a phone, and/or generally any electronic device. Suchan electronic system 700 includes various types of computer readablemedia and interfaces for various other types of computer readable media.The electronic system 700 includes a bus 708, one or more processingunit(s) 712, a system memory 704, a read-only memory (ROM) 710, apermanent storage device 702, an input device interface 714, an outputdevice interface 706, one or more network interface(s) 716, and/orsubsets and variations thereof.

The bus 708 collectively represents all system, peripheral, and chipsetbuses that communicatively connect the numerous internal devices of theelectronic system 700. In one or more implementations, the bus 708communicatively connects the one or more processing unit(s) 712 with theROM 710, the system memory 704, and the permanent storage device 702.From these various memory units, the one or more processing unit(s) 712retrieves instructions to execute and data to process in order toexecute the processes of the subject disclosure. The one or moreprocessing unit(s) 712 can be a single processor or a multi-coreprocessor in different implementations.

The ROM 710 stores static data and instructions that are utilized by theone or more processing unit(s) 712 and other modules of the electronicsystem 700. The permanent storage device 702, on the other hand, may bea read-and-write memory device. The permanent storage device 702 may bea non-volatile memory unit that stores instructions and data even whenthe electronic system 700 is off. In one or more implementations, amass-storage device (such as a magnetic or optical disk and itscorresponding disk drive) may be used as the permanent storage device702.

In one or more implementations, a removable storage device (such as afloppy disk, flash drive, and its corresponding disk drive) may be usedas the permanent storage device 702. Like the permanent storage device702, the system memory 704 may be a read-and-write memory device.However, unlike the permanent storage device 702, the system memory 704may be a volatile read-and-write memory, such as random access memory(RAM). The system memory 704 may store one or more of the instructionsand/or data that the one or more processing unit(s) 712 may utilize atruntime. The processes of the subject disclosure may be stored in thesystem memory 704, the permanent storage device 702, and/or the ROM 710.From these various memory units, the one or more processing unit(s) 712retrieve instructions to execute and data to process in order to executethe processes of one or more implementations.

The bus 708 also connects to the input and output device interfaces 714and 706. The input device interface 714 enables a user to communicateinformation and select commands to the electronic system 700. Inputdevices that may be used with the input device interface 714 mayinclude, for example, alphanumeric keyboards and pointing devices (alsocalled “cursor control devices”). The output device interface 706 mayenable, for example, the display of images generated by the electronicsystem 700. Output devices that may be used with the output deviceinterface 706 may include, for example, printers and display devices,such as a liquid crystal display (LCD), a light emitting diode (LED)display, an organic light emitting diode (OLED) display, a flexibledisplay, a flat panel display, a solid state display, a projector, suchas a prism projector that may be included in a smart glasses device, orany other device for outputting information. One or more implementationsmay include devices that function as both input and output devices, suchas a touchscreen. In these implementations, feedback provided to theuser can be any form of sensory feedback, such as visual feedback,auditory feedback, or tactile feedback; and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

As shown in FIG. 7, bus 708 also couples electronic system 700 to one ormore networks (not shown) through one or more network interface(s) 716.The one or more network interface(s) may include an Ethernet interface,a WiFi interface, a Bluetooth interface, a Zigbee interface, amultimedia over coax alliance (MoCA) interface, a reduced gigabit mediaindependent interface (RGMII), or generally any interface for connectingto a network. In this manner, electronic system 700 can be a part of oneor more networks of computers (such as a local area network (LAN), awide area network (WAN), or an Intranet, or a network of networks, suchas the Internet. Any or all components of electronic system 700 can beused in conjunction with the subject disclosure.

FIG. 8A is a schematic block diagram illustrating one example ofcircuitry and software within a device (i.e., within a device housing)that supports at least one implementation of the subject technology.Specifically, device circuitry 811A may represent all or part of each ofthe devices, servers and network devices. It may also be used in wholeor in part within any other computing or networking device, system orThing (e.g., as per the Internet of Things) such as those found in FIG.9. The device circuitry 811A includes processing circuitry 841, memory843, user interface circuitry 857, communication interface 859, motionand location sensor interface circuitry 861, environment sensorinterface circuitry 863 and bio-sensor interfacing circuitry 865.

The user interface circuitry 857 couples the device circuitry 811A withone or more of the various possible types of user interface elementssuch as touch-screen assemblies, speakers, microphones, mice, keyboards,keypads, buttons, image/video capture elements and so on. Thecommunication interface 859 couples the device circuitry 811A with oneor more external, remote other devices and underlying device circuitryvia proprietary or Industry Standard defined wired or wirelesscommunication pathways. The sensor elements and assemblies (although notshown) associated with the sensor interface circuitries 861, 863 and 865may be integrated within the device circuitry 811A or located at a morebeneficial location within, on or outside of the device housing (e.g.,via a tether). Such sensor elements and assemblies couple with thedevice circuitry 811A via the sensor interface circuitries 861, 863 and865.

The memory 843 stores program code 845 and gathered context andbehavioral data 855. The program code 845 includes environment contextdata gathering code 847, prediction code 849, resultant reconfigurationsand application management code 851, and selectable and reconfigurableapplications code 853. The prediction code 849 includes user's priorbehaviors and bio-profiles 849 a and similar other users' behaviors andbio-profiles 849 b.

While often being engaged with other processing duties, the devicecircuitry 811A operates to collect and store in time relationshipsmotion, location, environment, bio, App and user interface data gatheredover time. Such collected and stored data being represented in FIG. 8 asthe gathered context and behavioral data 855 within the memory 843. Suchdata may be stored in raw form (as collected), compressed form, or in aprocessed form that identifies relationships between such data with userbehaviors and their associated context. These collection and storageoperations are carried out by the processing circuitry 841 in accordancewith the environment context data gathering code 847. The processingcircuitry 841 collects from various motion, location, environment andbio sensors via the sensor interface circuitries 861, 863 and 865. Appusage and data associated with the other processing duties of theprocessing circuitry 841 is directly collected and stored. In addition,via the communication interface 859, other neighboring and remotedevices also deliver for storage additional motion, location,environment and bio sensors data and App and user interface andinteraction data.

With all such data storage within the gathered context and behavioraldata 855, the processing circuitry 841, pursuant to the prediction code849, generates the user's prior behaviors and bio-profiles 849 a. Forexample, the gathered context and behavioral data 855 when analyzedmight reveal a repetitive, time relationship between a sensed event inone sensor data stream with a sensed event in another sensor datastream. Likewise, a sensed event in one sensor data stream may have arepetitive, time relationship with a particular user interfacingbehavior or App operation. A simple version, for example, might involvea repetitive, time relationship involving a first event trigger orduration followed by a delay window and, finally, by a different streamevent trigger or duration. The averages and deviations thereof beinggenerated through repetition (e.g., possibly repeating often daily oronly several times a year). Once identified, the processing circuitry841 saves the relationship as one of the user's prior behaviors andbio-profiles 849 a, e.g., within a profile database.

In addition to performing both foreground tasks (e.g., often unrelatedApp and OS duties) and background collection, analysis and profilegeneration duties, the processing circuitry 841 also utilizes thegenerated bio-profiles 849 a in a predictive manner. By encountering afirst event trigger or duration of one of the bio-profiles 849 a andknowing the expected time relationship to a second event, the processingcircuitry 841 can take a preempting or assisting action to eitherenhance or simplify the user's experience or to dissuade or distract theuser and change the expected outcome. For example, a user with highblood pressure issues may react negatively to certain web browserinteraction and positively to adventure movies. Identifying theserelationships within profiles, the processing circuitry 841 may react toa blood pressure threshold by attempting to dissuade that particular webbrowser interaction and possibly direct the user to their TV to watchthe adventure film. Thus, it can be appreciated that more complexprofiles involve two or more timing relationships, each between twoevents along with associated preference logic that drives the user fromone expected outcome to another or enhances the likelihood of theexpected outcome, and, in either case, simplifying the user interactionsrequired.

By testing the user's prior bio-profiles 849 a, a likelihood of successcan be established. Those deemed unsuccessful even after repeatedattempts may be flagged so that they will not be used again. Throughuser interfacing (e.g., pop up window interaction), the user may beoffered the opportunity to prevent the flagging. The user may alsoreview profiles, flag, unflag and further define or modify profilesthrough a profile user screen interaction as directed by the processingcircuitry 841 via the user interface circuitry 857.

Many types of user and environmental actions and activities havebio-impact significance but occur outside the realm of an electronicdevice, e.g., petting a dog or scolding a child. In other words, theyare hidden contexts with predictive relationships with bio-impactsignificance. That is, such activity can trigger a bio-impact (e.g.,elevated heart rate, frown, angry voice or posture, blood sugar balance,etc.), or such activity can be triggered by such bio-impact. To revealthe nature of the hidden context, the processing circuitry 841 inaccordance with the program code 845 delivers a pop-up query to requestuser labeling and event desirability. For example, the processingcircuitry 841 may identify a bio-impact and conclude that there is nouser-device interaction in time window correlation that could be causingthe bio-impact. Instead of discarding such hidden context, theprocessing circuitry 841 queries the user as to what is happening. Theuser might respond “petting my dog.” The query continues asking the userabout the repeatability of such activity, likely duration, locationlimitations, and desirability relating to repeating behaviors.Thereafter, the processing circuitry 841 can weave such user gathereddetails into a fully functional profile that can be used to dissuade theuser from other activities, alter a user's bio events and status, andcan help enhance or simplify such activities (e.g., offer advertising topurchase pet grooming items or chew toys, or offer ads for identifying abreed or purchasing a dog).

Similarly, the processing circuitry 841 can query a user to identifypreference logic. The bio-impacting relationships are identifiedautomatically by the processing circuitry 841, and, without more, can beutilized by the processing circuitry 841 to enhance or assist anupcoming user behavior. For example, a user that always relaxes,lowering blood pressure, by turning on the TV and seeks a family moviechannel after returning home for work can be automatically identified.The processing circuitry 841 need not understand the nature of therelationship, but merely reacts to a high blood pressure status andarrival to a home location by sending command signals via thecommunication interface 859 to turn on the television and select afamily movie channel so that the user is drawn to relax and view.However, without considering that low blood pressure status isbeneficial when above a certain threshold, the processing circuitry 841cannot make more appropriate decisions. For example, it could be equallylikely that turning the television to a honor movie channel might driveblood pressure higher. To know whether to pursue the honor or familychannel “coaxing,” the processing circuitry 841 draws preference logicor preference data from (i) hard-coding by programmers (that understandunderlying goals), (ii) programmer defined data, (iii) from userinteraction, and (iv) from data associated with the sensing elements orsensing devices. Such preference logic or preference data can also begenerated within another user's device circuitry and communicated viathe communication interface 859 for storage as the similar other users'behaviors and bio-profiles 849 b.

A series of data streams originate from any device associated with or invicinity of the user. Each data stream represents one of time stampedmotion, location, environmental and bio sensor data and App, OS(Operating System) or user input and interaction data. These are storedwithin the memory 843 for use by the processing circuitry 841 in itsidentification of event states (triggers or expectations) within eachstream. With event states identified, the processing circuitry 841attempts to identify repetitive relationships between two or morestreams. Some event states that lead off an event sequence over time arereferred to as a triggering event. Expected middling and following eventstates are referred to as triggered events. Also in addition to levelsand level ranges, an event state may also be defined as a downward orupward trend and other more complex stream data behaviors.

In addition to self-identified triggering and triggered events extractedfrom analyzing and time correlating stream data, the processingcircuitry 841 also receives event state definitions from the streamsources themselves. For example, a heart rate sensing wearable sends notonly heart rate stream data, but can identify preference logic and dataalong with corresponding one or many event states such as thoseacceptable for the user while inactive (sleeping, sitting) and active(walking, running). A sensor data stream can also identify such eventstates within the sensor data stream, or define its sensor data streamas comprising only such event states without more (e.g., where suchassociated processing duties occurs within such sensor device).Alternatively, such event state definitions and preference logic anddata can be downloaded or otherwise loaded as a software based driver(or embedded within an App or OS via hard coding or via data) into thememory 843 for pairing with any one or more of the data streams storedwithin the memory 843 as the gathered context and behavioral data 855.

To determine from one sensor's data stream an event state, theprocessing circuitry 811A automatically attempts to identify deviations,volatility and abnormalities from average sensor sample value data overtime. Such identification can itself be used for cross-sensor data tocompare with other (e.g., being defined by meeting or exceedingthreshold value or threshold change or rate of changes once, repeatedly,or continuously over a period of time with or without some type ofaveraging). The processing circuitry 811A stores these “self-identified”event states (triggering events and triggered events), within the memory843 as part of the bio-profiles 849 a. Sensor, user interface, App, OS,etc., related deliveries of event states are also stored as part of thebio-profiles 849 a. Other event states of relevance (those beingtriggering and triggered events) associated with other users' are alsostored with the memory 843 as part of the bio-profiles 849 b.

The preference logic and data is not needed for event data with anassociated bio-impact or user behavior that is not undesirable. Thus,without such knowledge of desirability, the processing circuitry 841merely identifies a trigger event and helps to coax, enhance, setup orsimplify a user's subsequent behavior or interaction associated with theexpected corresponding triggered event—even in advance of expecting suchtriggered event to occur. If preference logic and data is available, theprocessing circuitry 841 appropriately alters its response to thetriggering event by either (i) more aggressively attempting to coax,enhance, setup or simplify a user's subsequent behavior or interaction,(ii) attempts to dissuade through direct user pop-up interaction, or(iii) offers coaxing alternatives that include setup and simplificationof the user's alternate, subsequent behavior or interaction. With thelatter, the processing circuitry 841 selects such alternatives fromthose identified by stream data—often from user interaction dataassociated with Apps and various devices. Where a single triggeringevent is expected to lead to two or more triggered events wherein themost likely is undesired, the processing circuitry 841 chooses the mostdesirable and most likely acceptable (based on offer/acceptancefrequency) from the other triggered events. Alternates can also beselected when contextually appropriate even though they have norelationship to a pending (recently occurring) triggering event. Suchunrelated alternatives may thwart the effect of the triggered event inreplacing with another desired, positive behavior.

FIG. 8B is a schematic block diagram illustrating another example ofcircuitry and software within a device (i.e., within a device housing)that supports at least one implementation of the subject technology.Specifically, device circuitry 811B may represent all or part of anydevice that includes sensor data collection, such as a Thing, wearable,networking devices, smartphone, tablet, etc., as illustrated, forexample, in FIGS. 1 and 9. Regarding functionality and associatedcircuitry and software, the device circuitry 811B comprises one exampleof a subset of that found within the device circuitry 811A of FIG. 8A.Within that subset, the functionality described in correspondence withFIG. 8A is fully applicable to that of FIG. 8B. Other subsets are alsopossible as FIG. 8B is merely but one possibility.

The device circuitry 811B captures, time-stamps, and selectivelyperforms pre-processing of raw sensor stream data before forwarding samevia a communication interface 859 to the device circuitry 811A (FIG. 8A)for use in bio-impact profiling and prediction. More specifically, muchlike the device circuitry 811A (FIG. 8A), the device circuitry 811Bincludes processing circuitry 841, memory 843, the communicationinterface 859, environment sensor interface circuitry 863 and bio-sensorinterface circuitry 865. Here, the processing circuitry 841 has no needto carry out the bio-impact profiling and prediction as it will be doneby another device. Instead, the processing circuitry 841 focuses on anyforeground tasks needed to be handled while coordinating (often inbackground) the collection and time stamping of raw sensor data from thesensor interface circuitries 863 and 865. To carry out time-stamping,the processing circuitry 841 also attempts to synchronize its clockingwith any or all of other data stream collection devices and devices thatperforms bio-impact profiling or prediction—at least those involved inunderlying device group behavioral and bio-impact related activities forthe subject user. In this way, the device circuitry 811A (FIG. 8A) canreceive time stamped data streams from several devices each configuredwith the device circuitry 811B and be able to conduct cross data streamevent correlations with legitimate timing relationships.

The processing circuitry 841 stores within the memory 843 the raw sensordata from the sensor interface circuitries 863 and 865 with timestamping (or otherwise associated or stored in or with timingrelationships or indications) as the gathered context data 869. Thememory 843 also stores the program code 845 which further includes datapreprocessing & forwarding code 867. In some configurations, thegathered context data 869 (i.e., the raw, time-stamped sensor datastreams) can be directly delivered to a remote device via thecommunication interface 859 even without time stamping if real time(which may not require the gathered context data 869 to be more than asmall, outgoing communication queue. In other cases, the processingcircuitry 841 performs data pre-processing to compress and forward asensor stream with or without event identification, depending on aselected mode of operation. Likewise, depending on the mode, theprocessing circuitry 841 delivers preference logic and/or dataassociated with a given sensor stream to another device via thecommunication interface 859 to assist with such other device'sbio-impact and prediction processing. Selecting the mode of operation ofthe processing circuitry 841 can be made on the fly (adaptively) throughresponding to commands received from such other device via thecommunication interface 859.

As can be appreciated, even the device circuitry 811A can be configuredto perform similarly through similar mode selections. Thus, when a firstplurality of devices each with the device circuitry 811A encounter asecond plurality of devices each with the device circuitry 811B, throughmode selection each of the duties of data stream generation,pre-processing of data streams (if any), bio-impacting profilegeneration, real time triggering event detection, real time responsethrough selective profile application, and so on can be allocated toappropriate and capable ones of the devices within the overall group.Some may discontinue sensor stream generation and enter sleep modes tosave power with another parallel sensor in another device taking overthe data stream generation requirements. Some may handle no sensorprocessing but merely handle profile generation. Others may only detector respond to a triggering event detection by coordinating multipledevice actions through group commands. In other words, variousnetworking nodes (e.g., switches, access points, Things, cloud andserver systems, hand held and wearable devices, computers, homeappliances, entertainment equipment, and so on), can be configured withsome or all portions of the hardware and software illustrated in FIGS.8A and 8B and further configured by mode selection to enable, disable orotherwise configure such hardware and software to meet a current,dynamic need associated with at least one but often many devices. Allsuch devices forming a functional group to carry out the overallbio-impact related process. Such groupings of course changing asunderlying devices are added or dropped based on mobility, powerresource status, alternate duty load balancing, communication linkstatus, and so on.

FIG. 9 is a block diagram illustrating an example of bio-impactprocessing and dynamic underlying duties assignment that supports atleast one implementation of the subject technology. Therein, a mobiledevice 911, such as a tablet, smartphone or laptop, is carried by aroaming user from location to location over time as indicated by thedashed arrowed line. Initially, as illustrated, the mobile device 911has no network or direct communication pathways to any other device soall bio-impact related processing is performed internally. For example:(i) all built in environmental and bio-sensors are captured, (ii) userinput data is captured, (ii) App usage and state information iscaptured; (iii) all captured data is processed to identify triggeringevents and triggered events, (iv) preference logic and data are appliedwhen available to attempt to dissuade a user from carrying out anotherwise expected triggered events; (v) for some triggering events, themobile device 911 attempts to coax the user toward the expected(triggered event) behavior through setup, enhancement and otherwisesimplify the user's interactions needed to begin and carry out theexpected behavior; and (vi) prompt the user for hidden context relatedinformation and data to define preference logic and preference data.

As location and movement sensors and connectivity capabilities change,the duty assignment and scope changes. For example, when moving in rangeof the first access device 921 a, the mobile device 911 gains access tonot only those resources of the first access device 921 a itself, butalso to intermediate switching and routing node resources (not shown),resources of cloud or server systems 923, and resources of any othernetworked device illustrated. To enlist the services of other devices,the mobile device 911 sends requests for capability and cost data to anydevice under consideration. In this example, the mobile device 911 makesthe request to the first access device 921 a and cloud or server system923 with the goal being to minimize battery and processing resourceconsumption. Through coordinated interactions amongst the three: (i) thecloud or server system 923 is assigned the task of identifying newprofiles based on stream data communicated from the mobile device 911,the duty of the first access device 921 a is to identify triggeringevents from the set of profiles, forward received data streams to theserver system 923, and communicate identified triggering events to themobile device 911; and (iii) the mobile device 911 forwards all datastreams and responds to triggering event identification (by the firstaccess device 921 a) by attempting to coax, dissuade or enhance anexpected behavior as defined by the underlying profile.

Again, as location and movement further bring about, the mobile device911, while still connected to the first access device 921 a, encountersthe first co-mobile device 913 a. For example, the user decides to don awearable device while still carrying the mobile device 911. Because thefirst co-mobile device 913 a may have greater battery resources or mayproduce better sensor data streams, it is selected to carry out datastream responsibilities for at least some of the sensors within themobile device 911. Once collected, the data streams can be forwardeddirectly to the first access device 921 a or relayed thereto through themobile device 911 (or through any other available device, e.g., where amesh interconnection is available). The corresponding “like-type”sensors within the mobile device 911 can then be shut down or stillforwarded to increase overall accuracy.

As battery resources wane or processing resource load balancing require,the mobile device 911 may also adapt by nominating the first co-mobiledevice 913 a to carry out much of its other duties—those associated withresponding to triggering event identification by attempting the coaxing,dissuading or enhancement of the expected behaviors. Such duties may allrequire an interaction with the mobile device 911 (via communicatedcommands) but may also involve carrying out internal commands as suchcoaxing, dissuading and enhancement can be performed in part by thefirst co-mobile device 913 a itself.

Later, through further roaming, Things 917 a and 917 b are encountered.The Thing 917 a being an environmental sensor, such as a temperaturesensor, while the Thing 917 b a refrigerator, for example. Onceencountered, both Things 917 a and 917 b may begin to send data streamsdirectly or through relaying to the first access device 921 a forprocessing. Once received, further bio-profile relationships becomeavailable for consideration, and the cloud or server system 923 gainsaccess to further data stream through which even furtherbio-relationships (bio-profiles) can be identified and commissioned backto the first access device 921 a for use in identifying triggeringevents from real time data streams.

Further roaming may find a user encountering a second co-mobile device913 b being donned along with stream drop outs along with directconnectivity to Things 917 a and 917 b and the first access device 921a. Picking up network access through a second access device 921 b offersmultiple options to going forward adaptation. First, the mobile device911 upon losing (or upon anticipating the loss) connectivity with thefirst access device 921 a, may still retain status quo functionality bycommunicating with the first access device 921 a indirectly—through thesecond access device 921 b to the first access device 921 a via one ormore communication networks 925. Handover of such functionality can alsobe selected where the mobile device 911 and the second access device 921b coordinate handover either via the underlying wireless cells or viathe communication networks 925. Such handover may only be in part, forexample, where the second access device 921 b handles some data streamprocessing or forwarding, while the first access device 921 a retainsthe rest of the duties.

Likewise, as some data streams drop out, others such as that from aThing 917 c, might become available. When some drop, certain relatedbio-profiles may become unavailable as they may be based on the droppedstreams. When added, a data stream becomes available for pre-identifiedbio-profile checking (if any exist) and for identifying furtherbio-profiles (e.g., in this example, by the cloud or server systems923).

Further mobility yields an encounter with a stationary computing device915 such as an in-home NAS (Network Attached Storage) system, a home orenterprise server, or a desktop computer. This encounter causes yet afurther adaptation which may result in the cloud or server system 923handing off its duties or portions thereof to the stationary computingdevice 915. Handovers with adaptation on overall bio-impact processingservices offered may take place nearly immediately upon new deviceencounters or over time.

To support such dynamic operation, the devices 911, 913 a-b, 917 a-c,921 a-b and 915 are each configured with embedded functionality 921through hardware and software such as that set forth in FIGS. 8A and 8B.In particular, such embedded functionality 921 includes one or more ofstorage of defined bio-impact profiles, bio-profile tailored Apps &bio-profile tailoring App, defined bio-impact profile based triggeringevent identification capabilities, full bio-impact profile generationcapabilities, and storage of user's environment, bio and device (App, OSand user input and interaction) data.

Likewise, the cloud or server systems 923 have various embeddedfunctionality supporting bio impact processing. For example, a cloudservice may be provided that can be tailored based on bio-impactprofiles specific to one user, all users or a group of similar users.Commands to tailor may originate from any of the devices illustrated inFIG. 9 (or underlying the communication networks 925) or internally fromwithin the cloud or server systems 923 (e.g., where the server systems923 itself carries out the duty of identifying triggering events withinreal time data based on pre-defined profiles. Similarly, Apps can betailored (through hard coding or via App data) that can then bedelivered to one of the devices of FIG. 9 for execution. That is, abio-impact profile may define a coaxing, dissuading or enhancingexperience via a specifically tailored App or App behavior that iscreated on the fly or beforehand for download and execution when socommanded. OS (Operating System) tailoring data can also be similarlycommunicated, for example, when coaxing is to be conducted by the OS.Any other tailored program element or element tailoring data can also oralternatively be used.

The cloud or server systems 923 also provides for distribution ofbio-impact solutions by sharing predefined bio-impact profiles. Suchpredefined profiles include those developed by other users in the mannerset forth herein, but also include hard-coded profiles defined byprogrammers via program code or program data. Thus, predefined profilesoriginate from (i) the user's equipment and environment, (ii) other andsimilar other users' equipment and environments, or (iii) throughprogrammers' direct coding or data definitions.

FIG. 10 is a block diagram illustrating one example of triggering eventidentification based on all types of bio-impact profiles that supportsone or more implementations of the subject technology. Therein, a timebased bio impact identification, prediction and verification engine 1005receives several data streams from which new bio-profiles are generatedand previously defined profiles are applied. The functionality of theengine 1005 can be distributed across many devices or run fully withinone as preconfigured, initially set up, or through handover operations,as described previously. The engine 1005 represents another way ofviewing the bio-related functionality carried out by the devicecircuitries 811A (FIG. 8A) and 811B (FIG. B).

Specifically, as illustrated, the engine 1005 receives four categoriesof data streams and, within each category, one or multiple data streamscan be found. The four categories being 1) bio sensor related datastreams, 2) software and device usage related data streams, 3)environmental sensor related data streams, and 4) user input andinteraction data streams. No time stamping of data stream content isneeded if virtually real time delivery of all data streams uponcollection occurs. If this isn't the case, time stamping within datastream content is applied. Upon receipt by the engine 1005, timestamping may still be performed for all un-stamped data stream data toaccommodate longer term evaluations (e.g., for new bio-profileidentification). Thereafter, three processes take place. The first is toattempt to identify new bio-profiles from all of the incoming datastreams that can be used for the first process. The second is to attemptto match all predefined bio-profiles with one or more relevant incomingdata streams. Lastly, the engine 1005 attempts to verify other user'sbio-profiles to make decisions about long term inclusion in the overallactive profile set.

Regarding the first process, the engine 1005 attempts to identify data“conditions” by identifying deviations, volatility and statisticalabnormalities, e.g., from averaging with various windows of raw values,rates of change, and so on. These conditions may or may not comprise atriggering or a triggered event from a bio-profile point of view. Inaddition to those generated by the engine 1005, conditions can bedefined and delivered from other sources, such as by the source of thedata stream itself. For example, a blood pressure sensor may have amanufacturer's defined set of condition parameters that can becommunicated in various ways to the engine 1005, including as part ofsetup or even within the data stream itself (as setup or midstream asconditions occur as identified by the data stream source).

With all conditions identified in each of the data streams based onanalysis or through received condition parameters, a time based analysisto find inter and intra stream repeatable relationships betweenconditions can be exposed along with probabilities of triggering typeconditions (“triggering events”) actually yielding expected triggeredtype conditions (“triggered events”). This may be as simple as onetriggering event causing a triggered event thirty seconds later at a 62%likelihood. More complexity enters the scene as it may take two or moreconditions to trigger one or more other conditions. All such complexityfalls within the definition of each predefined bio-profile. Onceidentified and reduced to a bio-profile format, the engine 1005 mayinitiate a user interaction to query for preference logic and data, asdescribed previously. Any retrieved preference logic and data iscombined into the bio-profile format. Both bio-profiles with and withoutpreference logic and data are saved as predefined bio-profiles, thespecific user's generated prediction profiles 1035, for use by thesecond process performed by the engine 1005.

For example, the engine 1005 identifies an App condition 1023 as atriggering event that when associated with a high room temperature, theenvironmental condition 1025 triggers within 2 minutes a high heart ratecondition 1021. For example, playing a video game during extremely hotweather may cause a child with a heart defect to enter fibrillation. Toavoid the bio condition 1021 which is the expected triggered event, analternative might be offered (as defined in supplemental responseinformation). The engine 1005 first recognized the relationship andconstructs a predefined profile in response. Similarly, a bio conditionmay comprise a triggering event of another bio condition or of an Appcondition. For example, when someone's blood pressure is dropping, theymay tap a thermostat to check and raise the ambient temperature. Aprofile might identify the drop in blood pressure, automatically adjustthe thermostat upward, and further prompt the user through pop-ups thatthey may need to take their medication. Also, as mentioned previously,some events may correspond to hidden contexts which can be probedthrough user querying to build more appropriate bio-profiles. Thus, allbio-profiles can involve one or more biometric data triggering andbiometric data triggered events. Non-bio related profiles can also beintegrated and supported by the engine 1005 and function in similar waysas the bio-profiles as described herein.

Regarding such second process, the engine 1005 evaluates in real timeonly those streams that are needed (and only when needed) to attempt toidentify conditions that seem to fall within the triggered eventdefinitions of the active, the predefined profiles 1035. Onceidentified, the engine 1005 will take actions based on whether or notpreference logic and data are present and in conformance therewith ifpresent. For example, if not present, the engine 1005 responds to anidentification of a condition corresponding to a triggering event of afirst bio-profile by carrying out actions that will help coax, enhance,setup and otherwise prepare for the expected triggered condition definedwithin the bio-profile. Similarly, if preference logic and data arepresent, the engine 1005 may respond in conformance to attempt todissuade or distract the user or otherwise attempt to prevent theexpected triggered event from occurring.

In addition, some predefined profiles also include supplemental responseinformation as to what steps are to be taken to avoid, coax or enhancean expected triggered event. Like the preference logic and data, thissupplemental information can be gathered through (i) query interactionwith the user, (ii) downloaded or selected from a set of predefinedalternative options, and (iii) selected based on secondary, less likelyexpected results (as mentioned previously). Selections based on suchgathered supplemental information options can be made automatically bythe engine 1005 or comprise part of a user interaction wherein the usermakes the choice. For example, the engine 1005 might identify atriggering event and deliver a pop window to the user to offer upseveral device environment configurations to support a correspondingseveral user activities that may or may not include the otherwiseexpected triggered event (depending on the preference logic and data).

Regarding the third process, the engine 1005 receives unverified hardcoded and other users' bio-profiles 1043 from a cloud, server, App basedor other sharing source 1041. These bio-profiles include triggeringevent and triggered event definition data, and, depending on theparticular profile, may include preference logic and data andsupplemental information regarding the response to an identifiedtriggering event. Because such profiles are unverified or untested withthe current user, they may prove ineffective as the triggered event andeven the underlying triggering event may not occur. Even so, because ofthe complexity of creating enriched bio-profiles, it is worthwhile toshare those that are effective with all or at least similar users to tryout. This try-out, or verification attempt, is carried out by the engine1005. If attempts to identify a shared profile's triggering event provefruitless, such shared profile can be discarded or flagged as inactive.If such triggering event is detected, a search for significantstatistical repeatability in producing the corresponding triggered eventis sought. If not found, again, the shared profile can be discarded orflagged as inactive. Lastly, if both the triggered and triggering eventsare identified, then the prediction profile can be flagged as verifiedand represented by verified prediction profiles 1034.

The engine 1005 also delivers prediction profiles 1051 to some of theone or groups of user equipment 1033 for their internal use inbio-profile processing. To some other of the ones and groups of userequipment 1033, the engine 1005 delivers real time tailoring commands1053 that attempt to adapt a user's current environment to enhance,adapt or avoid an expected user's behavior.

Implementations within the scope of the present disclosure can bepartially or entirely realized using a tangible computer-readablestorage medium (or multiple tangible computer-readable storage media ofone or more types) encoding one or more instructions. The tangiblecomputer-readable storage medium also can be non-transitory in nature.

The computer-readable storage medium can be any storage medium that canbe read, written, or otherwise accessed by a general purpose or specialpurpose computing device, including any processing electronics and/orprocessing circuitry capable of executing instructions. For example,without limitation, the computer-readable medium can include anyvolatile semiconductor memory, such as RAM, DRAM, SRAM, T-RAM, Z-RAM,and TTRAM. The computer-readable medium also can include anynon-volatile semiconductor memory, such as ROM, PROM, EPROM, EEPROM,NVRAM, flash, nvSRAM, FeRAM, FeTRAM, MRAM, PRAM, CBRAM, SONOS, RRAM,NRAM, racetrack memory, FJG, and Millipede memory.

Further, the computer-readable storage medium can include anynon-semiconductor memory, such as optical disk storage, magnetic diskstorage, magnetic tape, other magnetic storage devices, or any othermedium capable of storing one or more instructions. In one or moreimplementations, the tangible computer-readable storage medium can bedirectly coupled to a computing device, while in other implementations,the tangible computer-readable storage medium can be indirectly coupledto a computing device, e.g., via one or more wired connections, one ormore wireless connections, or any combination thereof.

Instructions can be directly executable or can be used to developexecutable instructions. For example, instructions can be realized asexecutable or non-executable machine code or as instructions in ahigh-level language that can be compiled to produce executable ornon-executable machine code. Further, instructions also can be realizedas or can include data. Computer-executable instructions also can beorganized in any format, including routines, subroutines, programs, datastructures, objects, modules, applications, applets, functions, etc. Asrecognized by those of skill in the art, details including, but notlimited to, the number, structure, sequence, and organization ofinstructions can vary significantly without varying the underlyinglogic, function, processing, and output.

While the above discussion primarily refers to microprocessor ormulti-core processors that execute software, one or more implementationsare performed by one or more integrated circuits, such as ASICs orFPGAs. In one or more implementations, such integrated circuits executeinstructions that are stored on the circuit itself.

Those of skill in the art would appreciate that the various illustrativeblocks, modules, elements, components, methods, and algorithms describedherein may be implemented as electronic hardware, computer software, orcombinations of both. To illustrate this interchangeability of hardwareand software, various illustrative blocks, modules, elements,components, methods, and algorithms have been described above generallyin terms of their functionality. Whether such functionality isimplemented as hardware or software depends upon the particularapplication and design constraints imposed on the overall system.Skilled artisans may implement the described functionality in varyingways for each particular application. Various components and blocks maybe arranged differently (e.g., arranged in a different order, orpartitioned in a different way) all without departing from the scope ofthe subject technology.

It is understood that any specific order or hierarchy of blocks in theprocesses disclosed is an illustration of example approaches. Based upondesign preferences, it is understood that the specific order orhierarchy of blocks in the processes may be rearranged, or that allillustrated blocks be performed. Any of the blocks may be performedsimultaneously. In one or more implementations, multitasking andparallel processing may be advantageous. Moreover, the separation ofvarious system components in the embodiments described above should notbe understood as requiring such separation in all embodiments, and itshould be understood that the described program components and systemscan generally be integrated together in a single software product orpackaged into multiple software products.

As used in this specification and any claims of this application, theterms “base station”, “receiver”, “computer”, “server”, “processor”, and“memory” all refer to electronic or other technological devices. Theseterms exclude people or groups of people. For the purposes of thespecification, the terms “display” or “displaying” means displaying onan electronic device.

As used herein, the phrase “at least one of” preceding a series ofitems, with the term “and” or “or” to separate any of the items,modifies the list as a whole, rather than each member of the list (e.g.,each item). The phrase “at least one of” does not require selection ofat least one of each item listed; rather, the phrase allows a meaningthat includes at least one of any one of the items, and/or at least oneof any combination of the items, and/or at least one of each of theitems. By way of example, the phrases “at least one of A, B, and C” or“at least one of A, B, or C” each refer to only A, only B, or only C;any combination of A, B, and C; and/or at least one of each of A, B, andC.

The predicate words “configured to”, “operable to”, and “programmed to”do not imply any particular tangible or intangible modification of asubject, but, rather, are intended to be used interchangeably. In one ormore implementations, a processor configured to monitor and control anoperation or a component may also mean the processor being programmed tomonitor and control the operation or the processor being operable tomonitor and control the operation. Likewise, a processor configured toexecute code can be construed as a processor programmed to execute codeor operable to execute code.

Phrases such as an aspect, the aspect, another aspect, some aspects, oneor more aspects, an implementation, the implementation, anotherimplementation, some implementations, one or more implementations, anembodiment, the embodiment, another embodiment, some embodiments, one ormore embodiments, a configuration, the configuration, anotherconfiguration, some configurations, one or more configurations, thesubject technology, the disclosure, the present disclosure, othervariations thereof and alike are for convenience and do not imply that adisclosure relating to such phrase(s) is essential to the subjecttechnology or that such disclosure applies to all configurations of thesubject technology. A disclosure relating to such phrase(s) may apply toall configurations, or one or more configurations. A disclosure relatingto such phrase(s) may provide one or more examples. A phrase such as anaspect or some aspects may refer to one or more aspects and vice versa,and this applies similarly to other foregoing phrases.

The word “exemplary” is used herein to mean “serving as an example,instance, or illustration.” Any embodiment described herein as“exemplary” or as an “example” is not necessarily to be construed aspreferred or advantageous over other embodiments. Furthermore, to theextent that the term “include,” “have,” or the like is used in thedescription or the claims, such term is intended to be inclusive in amanner similar to the term “comprise” as “comprise” is interpreted whenemployed as a transitional word in a claim.

All structural and functional equivalents to the elements of the variousaspects described throughout this disclosure that are known or latercome to be known to those of ordinary skill in the art are expresslyincorporated herein by reference and are intended to be encompassed bythe claims. Moreover, nothing disclosed herein is intended to bededicated to the public regardless of whether such disclosure isexplicitly recited in the claims. No claim element is to be construedunder the provisions of 35 U.S.C. § 112, sixth paragraph, unless theelement is expressly recited using the phrase “means for” or, in thecase of a method claim, the element is recited using the phrase “stepfor.”

The previous description is provided to enable any person skilled in theart to practice the various aspects described herein. Variousmodifications to these aspects will be readily apparent to those skilledin the art, and the generic principles defined herein may be applied toother aspects. Thus, the claims are not intended to be limited to theaspects shown herein, but are to be accorded the full scope consistentwith the language claims, wherein reference to an element in thesingular is not intended to mean “one and only one” unless specificallyso stated, but rather “one or more.” Unless specifically statedotherwise, the term “some” refers to one or more. Pronouns in themasculine (e.g., his) include the feminine and neuter gender (e.g., herand its) and vice versa. Headings and subheadings, if any, are used forconvenience only and do not limit the subject disclosure.

What is claimed is:
 1. A method performed by a processing device, themethod comprising: generating a user behavioral predictive model basedat least on historical behaviors of a user and associated historicalenvironmental profiles, at least one of the historical behaviors beingdeterminable from sensor data received from a sensor device associatedwith the user; obtaining an environmental profile of an environmentassociated with a user; determining a predicted behavior of the user byapplying the environmental profile to the user behavioral predictivemodel; determining information related to the predicted behavior of theuser; filtering the information related to the predicted behavior of theuser based at least in part on a recent behavior of the user; andproviding the information related to the predicted behavior to the user.2. The method of claim 1, further comprising: obtaining a currentbiometric profile of the user based at least on biometric data receivedfrom the sensor device, wherein the sensor device is coupled to theuser; determining a suggested behavior of the user based at least on theenvironmental profile and the current biometric profile of the user; andperforming an action to facilitate conforming the predicted behavior ofthe user to the suggested behavior of the user.
 3. The method of claim1, further comprising: receiving a behavioral policy associated with theuser; determining whether the predicted behavior of the user conformswith the behavioral policy; and impeding the predicted behavior when thepredicted behavior does not conform to the behavioral policy.
 4. Themethod of claim 1, further comprising: facilitating the predictedbehavior of the user.
 5. The method of claim 4, further comprising:receiving biometric data from the sensor device associated with theuser; and further filtering the information related to the predictedbehavior of the user based on the biometric data.
 6. The method of claim1, wherein the environmental profile comprises at least one of alocation, a time of day, proximal device information, or recentbehavioral information.
 7. The method of claim 6, wherein obtaining theenvironmental profile associated with the user comprises: receiving datafrom at least one of the sensor device associated with the user or aproximal device that the user is interacting with; and obtaining theenvironmental profile associated with the user based at least on thereceived data.
 8. The method of claim 1, further comprising: determiningan expected biometric profile of the user based at least on theenvironmental profile; obtaining a current biometric profile of theuser; and performing an action to facilitate conforming the currentbiometric profile of the user to the expected biometric profile.
 9. Themethod of claim 8, further comprising: obtaining another currentbiometric profile of another user; and performing the action that isexpected to facilitate conforming the current biometric profile of theuser and the another current biometric profile of the another user tothe expected biometric profile.
 10. The method of claim 8, furthercomprising: obtaining the current biometric profile of the user byreceiving biometric data from the sensor device, wherein the sensordevice is coupled to the user; and receiving sensor data from at leastone sensor device to obtain the environmental profile of theenvironment.
 11. The method of claim 8, further comprising: generating auser biometric model based at least on a plurality of recommendedbiometric profiles associated with a plurality of environment profiles,the plurality of recommended biometric profiles being adjusted based atleast on historical biometric profile data of the user; and determiningthe expected biometric profile of the user by applying the environmentalprofile to the user biometric model.
 12. The method of claim 8, furthercomprising: receiving a plurality of biometric profiles of the user thatare mapped to a plurality of behaviors of the user; and determining abiometric profile adjustment for conforming the current biometricprofile to the expected biometric profile, wherein performing the actionfacilitates one of the plurality of behaviors of the user that is mappedto one of the plurality of biometric profiles that achieves thebiometric profile adjustment.
 13. The method of claim 8, wherein theenvironment is associated with additional users and the method furthercomprises: receiving expected biometric profiles and current biometricprofiles of the additional users from devices of the additional users;and performing the action to facilitate conforming the current biometricprofiles of the additional users and the user to the expected biometricprofiles of the additional users and the user, respectively.
 14. Themethod of claim 13, further comprising: assimilating preferences of theadditional users received from the devices of the additional users; andperforming the action to facilitate the conforming based at least inpart on the assimilated preferences of the additional users.
 15. Themethod of claim 14, further comprising: determining a subset of thepreferences that are shared by a threshold number of the additionalusers; and performing the action to facilitate the conforming based atleast in part on the subset of the preferences of the additional users.16. The method of claim 14, further comprising: providing, for display,the preferences of the additional users; receiving user input withrespect to the displayed preferences of the additional users; andperforming the action to facilitate the conforming based at least inpart on the received user input.
 17. The method of claim 8, furthercomprising: gathering and storing time-related data, the time-relateddata including bio-sensing data, user interaction data and softwareapplication data; processing the time-related data to identifybio-impacting relationships from which the expected biometric profile ofthe user is determinable; identifying the current biometric profile fromthe bio-sensing data; and responding to the identification by performingthe action in an attempt to alter future bio-sensing data.
 18. Themethod of claim 8, further comprising: storing, with a timingrelationship, first data, the first data including bio-sensing relateddata and software application related data gathered over a period oftime; processing the first data to identify software application relateddata as having a first bio-impact; and performing the action to cause afuture generation of the software application related data to cause thefirst bio-impact in order to facilitate conforming the current biometricprofile of the user to the expected biometric profile.
 19. The method ofclaim 8, further comprising: storing, with a timing relationship,gathered data, the gathered data including bio-sensing related data anddevice operational data collected over time; processing the gathereddata to identify a predictive association between a first portion of thebio-sensing related data and a first portion of the device operationaldata; and performing the action to cause a future generation of thedevice operational data in response to obtaining the current biometricprofile of the user, wherein the current biometric profile of the usercomprises future counterparts of the first portion of the bio-sensingrelated data.
 20. The method of claim 19, wherein conforming the currentbiometric profile of the user to the expected biometric profile of theuser comprises maintaining the current biometric profile of the user.21. The method of claim 19, wherein the action comprises at least one ofsending a communication to a remote device, providing an offer to auser, performing a software reconfiguration, or performing a softwareselection.
 22. The method of claim 19, further comprising: process thegathered data to identify the predictive association between the firstportion of the bio-sensing related data, the first portion of the deviceoperational data, and at least one non-bio-sensing context informationitem.
 23. The method of claim 19, wherein the gathered data originatesfrom at least one of within the processing device or from otherenvironmental devices.
 24. A computer program product comprisinginstructions stored in a tangible computer-readable storage medium, theinstructions comprising: instructions for obtaining a user behavioralpredictive model that is configured to predict user behavior based atleast on a biometric data item collected from a user; instructions forcollecting at least one current biometric data item from the user;instructions for applying the at least one current biometric data itemto the user behavioral predictive model to determine a predicted userbehavior; and instructions for providing information to the user basedat least on the predicted user behavior and an environmental variableassociated with the user.
 25. The computer program product of claim 24,wherein the environmental variable is indicative of at least one of alocation of the user, a time of day, a device in proximity to the user,data received from the device in proximity to the user, or a recentbehavior performed by the user.
 26. The computer program product ofclaim 24, wherein the instructions for providing the information relatedat least to the predicted user behavior and the environmental variableassociated with the user further comprises: instructions for determininga set of information that may be relevant to the user based at least onthe predicted user behavior; and instructions for selecting theinformation from the set of information based at least on theenvironmental variable.
 27. The computer program product of claim 24,wherein the instructions for obtaining the user behavioral predictivemodel further comprises: instructions for receiving a plurality ofhistorical user behavioral data items that describe a plurality of userbehaviors performed by the user, wherein each of the plurality ofhistorical user behavioral data items is associated with at least one ofa plurality of biometric data items that was collected temporallyproximal to a time when each of the corresponding plurality of userbehaviors was performed by the user; and instructions for generating theuser behavioral predictive model based at least in part on the pluralityof historical user behavioral data items and the associated plurality ofbiometric data items.
 28. The computer program product of claim 27, theinstructions further comprising: instructions for augmenting the userbehavioral predictive model based at least on a location of the user atthe time when each of the plurality of user behaviors were performed bythe user, wherein the augmented user behavioral predictive model isconfigured to predict user behavior based at least on the at least onebiometric collected from the user and a current location of the userwhen the biometric data item is collected.
 29. The computer programproduct of claim 28, wherein at least one of the plurality of historicaluser behavioral items is associated with a set of biometric data itemscollected over a period of time that collectively describe a biologicalrhythm.
 30. A method performed by a processing device, the methodcomprising: storing, with a timing relationship, data, the dataincluding bio-sensing data and software application data gathered over aperiod of time; processing the data to identify bio-impactingrelationships; identifying a triggered event from the bio-impactingrelationships, the triggered event being an event that results from achange in the bio-sensing data; and performing an action in an attemptto cause a change in future bio-sensing data to prevent a futureoccurrence of the triggered event, the action being determined based atleast in part on the triggered event.
 31. The method of claim 30,further comprising: processing the data to identify software applicationrelated data as having a first bio-impact; and causing a futuregeneration of the software application related data to cause the firstbio-impact.
 32. The method of claim 31, wherein the causing comprises atleast one of: sending communications to remote devices, an offer to auser, a software reconfiguration, or a software selection.
 33. Themethod of claim 30, wherein the data further comprises user interactiondata, and the method further comprises gathering the data from at leastone of the processing device or another device.
 34. The method of claim30, wherein the data further comprises device operational data, and themethod further comprises: processing the data to identify a predictiveassociation between a first portion of the bio-sensing data and a firstportion of the device operational data; and causing a future generationof the device operational data after encountering future counterparts ofthe first portion of the bio-sensing data.
 35. The method of claim 34,wherein the predictive association is between the first portion of thebio-sensing data, the first portion of the device operational data andnon-bio-sensing context information.
 36. A device comprising: at leastone processor configured to: obtain an environmental profile of anenvironment associated with a user; determine an expected biometricprofile of the user based at least on the environmental profile; obtaina current biometric profile of the user; and perform an action tofacilitate conforming the current biometric profile of the user to theexpected biometric profile.
 37. The device of claim 36, wherein the atleast one processor is further configured to: obtain another currentbiometric profile of another user; and perform the action that isexpected to facilitate conforming the current biometric profile of theuser and the another current biometric profile of the another user tothe expected biometric profile.
 38. The device of claim 37, wherein theat least one processor is further configured to: obtain the currentbiometric profile of the user by receiving biometric data from a sensordevice, wherein the sensor device is coupled to the user; and receivesensor data from at least one sensor device to obtain the environmentalprofile of the environment.
 39. The device of claim 36, wherein the atleast one processor is further configured to: generate a user biometricmodel based at least on a plurality of recommended biometric profilesassociated with a plurality of environment profiles, the plurality ofrecommended biometric profiles being adjusted based at least onhistorical biometric profile data of the user; and determine theexpected biometric profile of the user by applying the environmentalprofile to the user biometric model.
 40. The device of claim 36, whereinthe at least one processor is further configured to: receive a pluralityof biometric profiles of the user that are mapped to a plurality ofbehaviors of the user; and determine a biometric profile adjustment forconforming the current biometric profile to the expected biometricprofile, wherein performing the action facilitates one of the pluralityof behaviors of the user that is mapped to one of the plurality ofbiometric profiles that achieves the biometric profile adjustment. 41.The device of claim 36, wherein the environment is associated withadditional users and the at least one processor is further configuredto: receive expected biometric profiles and current biometric profilesof the additional users from devices of the additional users; andperform the action to facilitate conforming the current biometricprofiles of the additional users and the user to the expected biometricprofiles of the additional users and the user, respectively.
 42. Thedevice of claim 41, wherein the at least one processor is furtherconfigured to: assimilate preferences of the additional users receivedfrom the devices of the additional users; and perform the action tofacilitate the conforming based at least in part on the assimilatedpreferences of the additional users.
 43. The device of claim 42, whereinthe at least one processor is further configured to: determine a subsetof the preferences that are shared by a threshold number of theadditional users; and perform the action to facilitate the conformingbased at least in part on the subset of the preferences of theadditional users.
 44. The device of claim 42, wherein the at least oneprocessor is further configured to: provide, for display, thepreferences of the additional users; receive user input with respect tothe displayed preferences of the additional users; and perform theaction to facilitate the conforming based at least in part on thereceived user input.
 45. The device of claim 36, wherein the at leastone processor is further configured to: gather and store time-relateddata, the time-related data including bio-sensing data, user interactiondata and software application data; process the time-related data toidentify bio-impacting relationships from which the expected biometricprofile of the user is determinable; identify the current biometricprofile from the bio-sensing data; and respond to the identification byperforming the action in an attempt to alter future bio- sensing data.46. The device of claim 36, wherein the at least one processor isfurther configured to: store, with a timing relationship, first data,the first data including bio-sensing related data and softwareapplication related data gathered over a period of time; process thefirst data to identify software application related data as having afirst bio-impact; and perform the action to cause a future generation ofthe software application related data to cause the first bio-impact inorder to facilitate conforming the current biometric profile of the userto the expected biometric profile.
 47. The device of claim 36, whereinthe at least one processor is configured to conform the currentbiometric profile of the user to the expected biometric profile of theuser comprises by maintaining the current biometric profile of the user.48. The device of claim 36, wherein the at least one processor isfurther configured to: store, with a timing relationship, gathered data,the gathered data including bio-sensing related data and deviceoperational data collected over time; process the gathered data toidentify a predictive association between a first portion of thebio-sensing related data and a first portion of the device operationaldata; and performing the action to cause a future generation of thedevice operational data in response to obtaining the current biometricprofile of the user, wherein the current biometric profile of the usercomprises future counterparts of the first portion of the bio-sensingrelated data.
 49. The device of claim 48, wherein the action comprisesat least one of sending a communication to a remote device, providing anoffer to a user, performing a software reconfiguration, or performing asoftware selection.
 50. The device of claim 48, wherein the at least oneprocessor is further configured to: process the gathered data toidentify the predictive association between the first portion of thebio-sensing related data, the first portion of the device operationaldata, and at least one non-bio-sensing context information item.